{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MhoQ0WE77laV"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
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     "shell.execute_reply": "2022-08-31T04:53:21.749623Z"
    },
    "id": "_ckMIh7O7s6D"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:21.753758Z",
     "iopub.status.busy": "2022-08-31T04:53:21.753193Z",
     "iopub.status.idle": "2022-08-31T04:53:21.756399Z",
     "shell.execute_reply": "2022-08-31T04:53:21.755889Z"
    },
    "id": "vasWnqRgy1H4"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jYysdyb-CaWM"
   },
   "source": [
    "# 基本分类：对服装图像进行分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S5Uhzt6vVIB2"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/classification\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a></td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FbVhjPpzn6BM"
   },
   "source": [
    "本指南将训练一个神经网络模型，对运动鞋和衬衫等服装图像进行分类。即使您不理解所有细节也没关系；这只是对完整 TensorFlow 程序的快速概述，详细内容会在您实际操作的同时进行介绍。\n",
    "\n",
    "本指南使用了 [tf.keras](https://tensorflow.google.cn/guide/keras)，它是 TensorFlow 中用来构建和训练模型的高级 API。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:21.760833Z",
     "iopub.status.busy": "2022-08-31T04:53:21.760320Z",
     "iopub.status.idle": "2022-08-31T04:53:24.108930Z",
     "shell.execute_reply": "2022-08-31T04:53:24.108159Z"
    },
    "id": "dzLKpmZICaWN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.11.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## 导入 Fashion MNIST 数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DLdCchMdCaWQ"
   },
   "source": [
    "本指南使用 [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) 数据集，该数据集包含 10 个类别的 70,000 个灰度图像。这些图像以低分辨率（28x28 像素）展示了单件衣物，如下所示：\n",
    "\n",
    "<table>\n",
    "  <tr><td>     <img alt=\"Fashion MNIST sprite\" src=\"https://tensorflow.google.cn/images/fashion-mnist-sprite.png\"> </td></tr>\n",
    "  <tr><td align=\"center\">     <b>图 1.</b>  <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST 样本</a>（由 Zalando 提供，MIT 许可）。<br> </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST 旨在临时替代经典 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据集，后者常被用作计算机视觉机器学习程序的“Hello, World”。MNIST 数据集包含手写数字（0、1、2 等）的图像，其格式与您将使用的衣物图像的格式相同。\n",
    "\n",
    "本指南使用 Fashion MNIST 来实现多样化，因为它比常规 MNIST 更具挑战性。这两个数据集都相对较小，都用于验证某个算法是否按预期工作。对于代码的测试和调试，它们都是很好的起点。\n",
    "\n",
    "在本指南中，我们使用 60,000 张图像来训练网络，使用 10,000 张图像来评估网络学习对图像进行分类的准确程度。您可以直接从 TensorFlow 中访问 Fashion MNIST。直接从 TensorFlow 中导入和加载 Fashion MNIST 数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.113812Z",
     "iopub.status.busy": "2022-08-31T04:53:24.113099Z",
     "iopub.status.idle": "2022-08-31T04:53:24.508248Z",
     "shell.execute_reply": "2022-08-31T04:53:24.507495Z"
    },
    "id": "7MqDQO0KCaWS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "29515/29515 [==============================] - 0s 2us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26421880/26421880 [==============================] - 4s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "5148/5148 [==============================] - 0s 0s/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4422102/4422102 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9FDsUlxCaWW"
   },
   "source": [
    "加载数据集会返回四个 NumPy 数组：\n",
    "\n",
    "- `train_images` 和 `train_labels` 数组是*训练集*，即模型用于学习的数据。\n",
    "- *测试集*、`test_images` 和 `test_labels` 数组会被用来对模型进行测试。\n",
    "\n",
    "图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。*标签*是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装*类*：\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>标签</th>\n",
    "    <th>类</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T恤/上衣</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>裤子</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>套头衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>连衣裙</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>外套</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>凉鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>衬衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>运动鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>包</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>短靴</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "每个图像都会被映射到一个标签。由于数据集不包括*类名称*，请将它们存储在下方，供稍后绘制图像时使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.512588Z",
     "iopub.status.busy": "2022-08-31T04:53:24.512315Z",
     "iopub.status.idle": "2022-08-31T04:53:24.515817Z",
     "shell.execute_reply": "2022-08-31T04:53:24.515239Z"
    },
    "id": "IjnLH5S2CaWx"
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## 浏览数据\n",
    "\n",
    "在训练模型之前，我们先浏览一下数据集的格式。以下代码显示训练集中有 60,000 个图像，每个图像由 28 x 28 的像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.519208Z",
     "iopub.status.busy": "2022-08-31T04:53:24.518643Z",
     "iopub.status.idle": "2022-08-31T04:53:24.527050Z",
     "shell.execute_reply": "2022-08-31T04:53:24.526533Z"
    },
    "id": "zW5k_xz1CaWX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "同样，训练集中有 60,000 个标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.530656Z",
     "iopub.status.busy": "2022-08-31T04:53:24.530086Z",
     "iopub.status.idle": "2022-08-31T04:53:24.534210Z",
     "shell.execute_reply": "2022-08-31T04:53:24.533436Z"
    },
    "id": "TRFYHB2mCaWb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "每个标签都是一个 0 到 9 之间的整数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.537171Z",
     "iopub.status.busy": "2022-08-31T04:53:24.536942Z",
     "iopub.status.idle": "2022-08-31T04:53:24.541031Z",
     "shell.execute_reply": "2022-08-31T04:53:24.540491Z"
    },
    "id": "XKnCTHz4CaWg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "测试集中有 10,000 个图像。同样，每个图像都由 28x28 个像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.544453Z",
     "iopub.status.busy": "2022-08-31T04:53:24.544002Z",
     "iopub.status.idle": "2022-08-31T04:53:24.547906Z",
     "shell.execute_reply": "2022-08-31T04:53:24.547287Z"
    },
    "id": "2KFnYlcwCaWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "测试集包含 10,000 个图像标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.551233Z",
     "iopub.status.busy": "2022-08-31T04:53:24.550743Z",
     "iopub.status.idle": "2022-08-31T04:53:24.554749Z",
     "shell.execute_reply": "2022-08-31T04:53:24.554128Z"
    },
    "id": "iJmPr5-ACaWn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## 预处理数据\n",
    "\n",
    "在训练网络之前，必须对数据进行预处理。如果您检查训练集中的第一个图像，您会看到像素值处于 0 到 255 之间："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.558291Z",
     "iopub.status.busy": "2022-08-31T04:53:24.557721Z",
     "iopub.status.idle": "2022-08-31T04:53:24.828210Z",
     "shell.execute_reply": "2022-08-31T04:53:24.827508Z"
    },
    "id": "m4VEw8Ud9Quh"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Fy/T4CRrAIBjWWGOrJ2SrLlnDQCAzTGyBgA4lqXwHjJxyvMpJGsAgGP55JKLN5gBAIBYY2QNAHAsVoMDAGBzPsslVxw8Z800OAAANsfIGgDgWJYV5mpwhywHJ1kDYervMS+WkeLqMI5JdnUax+zr6GscI0k7TlxmHPNFs3lBkynZ/2kc0xFCUQ53iA/ohFJgIzfpb8YxrZZ58Q/zK+ika7LNi3LUhthWd4iXe9ZMgwMAYHOMrAEAjhUvI2uSNQDAsVgNfhbvv/++pk+frtzcXLlcLr366qsB37csS8uWLdOAAQPUo0cPFRUVaceOHZHqLwAAfqcWmIWzOYFxsm5paVF+fr4qKyu7/P7y5cv1xBNPaNWqVfrwww/Vq1cvFRcXq7W1NezOAgAQj4ynwadOnaqpU6d2+T3LsrRy5Urdf//9uvHGGyVJf/zjH5Wdna1XX31VN9988xkxbW1tamtr83/d3Nxs2iUAQJw6OToO5551BDsTRRFdDb579241NDSoqKjIvy89PV0FBQXatGlTlzEVFRVKT0/3b3l5eZHsEgDgAnZqgVk4mxNENFk3NDRIkrKzswP2Z2dn+7/3j5YsWaKmpib/Vl9fH8kuAQDgeDFfDe7xeOTxeGLdDQCAA1kKrya1Q2bBIzuyzsnJkSQ1NjYG7G9sbPR/DwCASGEaPATDhg1TTk6Oqqur/fuam5v14YcfqrCwMJJNAQAQN4ynwY8dO6adO3f6v969e7dqa2uVkZGhwYMHa+HChXrkkUd0ySWXaNiwYVq6dKlyc3M1Y8aMSPYbAIC4mQc3TtYfffSRbrjhBv/XZWVlkqS5c+dqzZo1uu+++9TS0qI77rhDR44c0bXXXquqqiqlpKRErtdwLpf5lJPLbV64weo0L3ohSe6+5oUvvtfnU+OYg94045gj3p7GMX3cx41jJOlop/m/129OmPdvhGe/ccy240ONY/onmxfXkEI7f3vaM41jLvF0vQD3XJY3TjKOkaS8lG+MYzonXW92fGerVPOacTshCXcqO8TYyspKPfbYY2poaFB+fr5+85vfaMKECWc9/siRI/rFL36hl19+Wd98842GDBmilStXatq0aUG1Z5ysJ06cKOscD6a5XC499NBDeuihh0w/GgAAI7Eokblu3TqVlZVp1apVKigo0MqVK1VcXKy6ujplZZ1Zfa69vV3/9E//pKysLP3pT3/SwIED9de//lV9+vQJus2YrwYHAMBJVqxYoQULFmjevHmSpFWrVunNN9/U6tWr9bOf/eyM41evXq1vvvlGH3zwgZKSTpZDHTp0qFGblMgEADhWpFaDNzc3B2ynv1nzdO3t7dq6dWvAy78SEhJUVFR01pd//fu//7sKCwtVUlKi7OxsjRo1So8++qi83uDrpZOsAQDOZbnC3yTl5eUFvE2zoqKiy+YOHTokr9dr9PKvL7/8Un/605/k9Xr11ltvaenSpXr88cf1yCOPBP3HZBocABD36uvrlZb27cLPSL6sy+fzKSsrS7/97W/ldrs1btw4ff3113rsscdUXl4e1GeQrAEAjhWpBWZpaWkByfpsMjMz5Xa7jV7+NWDAACUlJcl92pMtl19+uRoaGtTe3q7k5OTztss0OADAuawIbAaSk5M1bty4gJd/+Xw+VVdXn/XlX9dcc4127twpn8/n3/fFF19owIABQSVqiWQNAICRsrIyPfPMM/rDH/6gzz77THfddZdaWlr8q8PnzJmjJUuW+I+/66679M033+iee+7RF198oTfffFOPPvqoSkpKgm6TaXAAgGOF+37vUGJnzZqlgwcPatmyZWpoaNDYsWNVVVXlX3S2d+9eJSR8OxbOy8vT22+/rUWLFmnMmDEaOHCg7rnnHi1evDjoNknWAABni8ErQ0tLS1VaWtrl92pqas7YV1hYqM2bN4fcHtPgAADYHCNrAIBjxWIaPBZI1gAA56LqFhAFITwQ6Uo0v0xDrbpVP/9y45jv93zdOOaD1oHGMf0TjxrHdFjmFcskaYCnyTgmNbvVOCaUSmIZiceMY456exjHSFLPhK5fOXkuofw9fTf5kHHMone+axwjSamjDhvHpCWZ3TH1desdVtfft3Di7Y971gAA2BwjawCAczENDgCAzcVJsmYaHAAAm2NkDQBwrtPKXIYc7wAkawCAY0Wq6pbdMQ0OAIDNMbIGADhXnCwwI1kDAJwrTu5ZMw0OAIDNMbIGADiWyzq5hRPvBCRrAIBzcc8aiDxXUrJxjK/VvEBEqDI/bTeOOeRNMo7pk3DcOCbZ5TWOaQ+xkMfVGbuNYw6GUCxj24lhxjGp7hPGMf0TzItrSFJeknnRi09b84xj3mq52Dhm/n95xzhGkp7/7T8ZxyRXfWB0fILVYdxGyLhnDQAA7ICRNQDAuZgGBwDA5uIkWTMNDgCAzTGyBgA4V5yMrEnWAADnYjU4AACwA0bWAADH4g1mAADYXZzcs2YaHAAAmyNZAwBgc0yDAwAcy6Uw71lHrCfRFd/J2hXaX5Mr0bxwg8sdwiRGgnmMr7XNvB2feYGIUFkd5oUyutP/+J9PGsfUd/YxjmnoMI/p4zYv/uEN8UfR5hPpxjEpCebFG/onNhvHNPvMC4aE6qgvxTimI4TiKaGcu8X9dhjHSNLLTUUhxdkWj24BAAA7iO+RNQDA2eJkNTjJGgDgXHGSrJkGBwDA5hhZAwAcizeYAQBgd0yDAwAAO2BkDQBwrjgZWZOsAQCOFS/3rJkGBwDA5hhZAwCcK05eN0qyBgA4F/esncWVaP5HsTo7Q2orlGIUlvl7+i9IJ26cYBxTP8O80MitV2wxjpGkhs5U45iPjw81jkl3nzCO6ZVgXqSl1TIvOiNJ+9r7GseEUowiI/GYcUxWCMU/vFZod/y+7jA/D6EIpUjLV53m506Sjv7wqHFMnz+G1FS34J41AACwhQtmZA0AiENMgwMAYHNhToM7JVkbT4O///77mj59unJzc+VyufTqq68GfP/222+Xy+UK2KZMmRKp/gIAEHeMk3VLS4vy8/NVWVl51mOmTJmi/fv3+7fnn38+rE4CANAlKwKbAxhPg0+dOlVTp0495zEej0c5OTlBfV5bW5va2r5d5drcbL7SEwAQp+LknnVUVoPX1NQoKytLl112me666y4dPnz4rMdWVFQoPT3dv+Xl5UWjSwAAOFbEk/WUKVP0xz/+UdXV1frVr36lDRs2aOrUqfJ6u35WdsmSJWpqavJv9fX1ke4SAOACdeo563A2J4j4avCbb77Z//+jR4/WmDFjdNFFF6mmpkaTJk0643iPxyOPxxPpbgAAcMGI+ktRhg8frszMTO3cuTPaTQEAcEGK+nPWX331lQ4fPqwBAwZEuykAQLyJkwVmxsn62LFjAaPk3bt3q7a2VhkZGcrIyNCDDz6omTNnKicnR7t27dJ9992niy++WMXFxRHtOAAA8fJucONk/dFHH+mGG27wf11WViZJmjt3rp5++ml98skn+sMf/qAjR44oNzdXkydP1sMPPxz1+9KhFuXoLokDgnuU7XQdw7KNY765vKdxzPGc0ErEjZ32mXHM7dnPGscc9KYZxyS5Qrse6jv6Gcdc0XOPccy7TSONYw4l9jaOCaVgiCRd3WuHccwRn/m1l5v4N+OYxTt/bByT3dO8eIUk/W7IW8YxHZbPOKauw/znY5PPbRwjSf//yPeMY15R/5Da6jYOSbjhME7WEydOlGWd/cy8/fbbYXUIAAAE4t3gAADn4p41AAD2Fi/3rKlnDQCAzTGyBgA4F9PgAADYG9PgAADAFkjWAADnilE968rKSg0dOlQpKSkqKCjQli1bgop74YUX5HK5NGPGDKP2SNYAAOeKQbJet26dysrKVF5erm3btik/P1/FxcU6cODAOeP27Nmje++9V9ddd51xmyRrAEDca25uDtja2trOeuyKFSu0YMECzZs3TyNHjtSqVavUs2dPrV69+qwxXq9Xt956qx588EENHz7cuH8kawCAY0WqnnVeXp7S09P9W0VFRZfttbe3a+vWrSoqKvLvS0hIUFFRkTZt2nTWfj700EPKysrS/PnzQ/pzshocAOBcEXp0q76+Xmlp39YhOFs9i0OHDsnr9So7O7B2Q3Z2tj7//PMuYzZu3Kjf//73qq2tDbmbJGsAgHNFKFmnpaUFJOtIOXr0qG677TY988wzyszMDPlzLphk3Tb1SuOYrF98GVJbY9O+Mo4Z2WOjcUyrL8k4JiWhwzjmLycGGsdI0nFfsnHMjnbz6mNNnebVnNwu88pHknSgPdU45vHdRec/6B9UT1hlHHP/vinGMQk9QvspdthrXuFrZu/mEFoyv8b/2+D3jWOGJ5974c/ZvNEywDhmX0df45jspCbjmKFJB41jJOmm1C+MY2xfdasbZWZmyu12q7GxMWB/Y2OjcnLO/Pm2a9cu7dmzR9OnT/fv8/lO/nxKTExUXV2dLrroovO2yz1rAIBjReqedbCSk5M1btw4VVdX+/f5fD5VV1ersLDwjONHjBihTz/9VLW1tf7thz/8oW644QbV1tYqLy8vqHYvmJE1ACAOxeB1o2VlZZo7d67Gjx+vCRMmaOXKlWppadG8efMkSXPmzNHAgQNVUVGhlJQUjRo1KiC+T58+knTG/nMhWQMAYGDWrFk6ePCgli1bpoaGBo0dO1ZVVVX+RWd79+5VQkJkJ65J1gAAx4rVu8FLS0tVWlra5fdqamrOGbtmzRrj9kjWAADnipOqWywwAwDA5hhZAwCcK05G1iRrAIBjuf6+hRPvBEyDAwBgc4ysAQDOxTQ4AAD2FqtHt7obyRoA4FyMrGPLlZgolyv47hU8+mfjNial/qdxjCQdt7ounXYuoRTlCKUgQCjSE4+HFNfWYX75HOiIfFWbrlzqaQgp7kdptcYx7z9ZYBxzbevdxjG7vv+scUz1CbdxjCQd7DT/e7p59/eNY7btDe69yKe7auhu45jRqV8bx0ihFZFJdbcaxyS5Oo1jWnzmP4ckaXOreZEWxJ5tkzUAAEFxyOg4HCRrAIBjxcs9ax7dAgDA5hhZAwCciwVmAADYG9PgAADAFhhZAwCci2lwAADsjWlwAABgC4ysAQDOxTQ4AAA2R7IGAMDe4uWetW2T9f67xsntSQn6+AfSf2PcxtpvrjKOkaS8lG+MY4YkHzKOye/xV+OYUKQmmBcekKTL0syLD7zRMsg4pubICOOYAUlHjGMk6X8fv8g45oUHHjOOuX3RfzeOKXzrTuOY5qGhLUvp7GX+Eywt/7BxzP1XvGkck+zyGscc8ZoX5JCkDE+LcUwfd2iFcUyFUlBIklITThjHuC+72Oh4y9sm7TBuBudg22QNAMB5MQ0OAIC9uSxLLiv0jBtObHfi0S0AAGyOkTUAwLmYBgcAwN7iZTU40+AAANgcI2sAgHMxDQ4AgL0xDQ4AAGyBkTUAwLmYBgcAwN7iZRqcZA0AcC5G1rHV84BP7mRf0Me/0TzWuI3hPQ4ax0jSoY5U45i3j402jhnU42/GMelu85f0X+xpMI6RpNrWPsYxVQe/YxyT26PZOKaxI904RpIOd/QyjjnuMy+o8PtfrzCOebyxyDjmRxnbjGMkKT/ZvCjHEZ/5Epi/tOcYxxz1BV/g55RWK8k4RpKaQigAkhrCv8EOy/xHsdsK/ufj6fokmBcaaR7dz+j4zo5WCnlEmG2TNQAAwXDKVHY4SNYAAOeyrJNbOPEOwKNbAADYnFGyrqio0JVXXqnU1FRlZWVpxowZqqurCzimtbVVJSUl6tevn3r37q2ZM2eqsbExop0GAED6djV4OJsTGCXrDRs2qKSkRJs3b9b69evV0dGhyZMnq6WlxX/MokWL9Prrr+ull17Shg0btG/fPt10000R7zgAAP7V4OFsDmB0z7qqqirg6zVr1igrK0tbt27V9ddfr6amJv3+97/X2rVr9f3vf1+S9Oyzz+ryyy/X5s2bddVVV53xmW1tbWpra/N/3dxsvvIXAIALWVj3rJuamiRJGRkZkqStW7eqo6NDRUXfPmIyYsQIDR48WJs2beryMyoqKpSenu7f8vLywukSACCOuHzhb04QcrL2+XxauHChrrnmGo0aNUqS1NDQoOTkZPXp0yfg2OzsbDU0dP0s75IlS9TU1OTf6uvrQ+0SACDeMA1+biUlJdq+fbs2btwYVgc8Ho88HvOXSgAAEC9CGlmXlpbqjTfe0HvvvadBgwb59+fk5Ki9vV1HjhwJOL6xsVE5OeZvKgIA4FxYDd4Fy7JUWlqqV155Re+++66GDRsW8P1x48YpKSlJ1dXV/n11dXXau3evCgsLI9NjAABOOfVSlHA2BzCaBi8pKdHatWv12muvKTU11X8fOj09XT169FB6errmz5+vsrIyZWRkKC0tTXfffbcKCwu7XAkOAEA4qLrVhaefflqSNHHixID9zz77rG6//XZJ0q9//WslJCRo5syZamtrU3FxsZ566injjvX+uk2Jia6gj/dZwR97yruHRhjHSFJ2ylHjmLGp5gvn6o6b3zr49ESuccy2xMHGMZLUw91hHJOe3Goc0yux7fwH/YPMJPO/I0ka5jlgHJPs8hrH/LnV/Jzf1b/GOGZvZ1/jGEl6veVS45i/HDe/9vommheV+LTZvJ3jncnGMZLU5jVf1tPaaV60J91j/u/iyoy/GsdIUp0GGMcczDe7Y+prTZBeNW4G52B0JVpBTBekpKSosrJSlZWVIXcKAICgUCITAAB7i5dpcAp5AABgc4ysAQDOFSclMknWAADHYhocAADYAiNrAIBzsRocAAB7YxocAADYAiNrAIBz+ayTWzjxDkCyBgA4F/esAQCwN5fCvGcdsZ5EF/esAQCwOduOrBM2fqIEV1LQx7/0v64xbmPpjS8Zx0jShiPm1breaDCvxNPc7jGO6d+zxTgmLcQKVRlJ5m2lh1BlKcXVaRzzt85exjGS1JYQ/DV3ijeE380b2tKNY/6P7xLjmA6f2zhGktpCiAulCts37ZnGMbk9moxjjnamGMdI0p6jGcYxh5p6G8e09jT/UbzRe5FxjCRNyflP45geB8yucW9bN45XeYMZAAD2xqNbAACgS5WVlRo6dKhSUlJUUFCgLVu2nPXYZ555Rtddd5369u2rvn37qqio6JzHd4VkDQBwLisCm6F169aprKxM5eXl2rZtm/Lz81VcXKwDBw50eXxNTY1mz56t9957T5s2bVJeXp4mT56sr7/+Oug2SdYAAMdyWVbYmyQ1NzcHbG1tbWdtc8WKFVqwYIHmzZunkSNHatWqVerZs6dWr17d5fHPPfecfvrTn2rs2LEaMWKEfve738nn86m6ujroPyfJGgAQ9/Ly8pSenu7fKioqujyuvb1dW7duVVFRkX9fQkKCioqKtGnTpqDaOn78uDo6OpSREfwCRhaYAQCcy/f3LZx4SfX19UpLS/Pv9ni6fhrn0KFD8nq9ys7ODtifnZ2tzz//PKgmFy9erNzc3ICEfz4kawCAY50+lR1qvCSlpaUFJOto+eUvf6kXXnhBNTU1SkkJ/pFCkjUAAEHKzMyU2+1WY2NjwP7Gxkbl5OScM/Zf//Vf9ctf/lLvvPOOxowZY9Qu96wBAM7VzavBk5OTNW7cuIDFYacWixUWFp41bvny5Xr44YdVVVWl8ePHmzUqRtYAACeLwRvMysrKNHfuXI0fP14TJkzQypUr1dLSonnz5kmS5syZo4EDB/oXqf3qV7/SsmXLtHbtWg0dOlQNDQ2SpN69e6t37+DeeEeyBgA4VizeYDZr1iwdPHhQy5YtU0NDg8aOHauqqir/orO9e/cqIeHbieunn35a7e3t+vGPfxzwOeXl5XrggQeCapNkDQCAodLSUpWWlnb5vZqamoCv9+zZE3Z7F0yyHr44uOfbTvfUJz8+/0FdtfXTOuOYqTnbjWO2NQ82jtkbQuGB/ziRaxwjSUkJ5s9L9ExqN45JCaFARLLbaxwjSQkhvM7IF0Ihj15u8/PQK/HsL2k4mwyPebEVSUp1txrHJLjCeX4meO4Q/o62NA0Nqa3snuZFbi5OO2Qc02mZLx8qTN9lHCNJq3dfbRyT/ZsPjI7vtDr0F+NWQkQhDwAA7M3lO7mFE+8ErAYHAMDmGFkDAJyLaXAAAGwuxMpZAfEOwDQ4AAA2x8gaAOBYkXo3uN2RrAEAzhUn96yZBgcAwOYYWQMAnMtSePWsnTGwJlkDAJyLe9YAANidpTDvWUesJ1HFPWsAAGzOviPrBLfkcgd/vM+8cEP6c5uNYyTp8HPmMX+aWWwcU/DzPxvH/Jeh/2EcMyK50ThGkpJCuFGUEsKLeHslmBfKaA3xN+1QfnvdeCLPOMYbQkvv/u1y45gjHT2MYySp8XiacUxSiMVTTPks8+vhRGdSSG01nUgxjnEnmF97rTWZxjG7/zLCOEaS0t8y/7lia3GyGty+yRoAgPPxSSEUvguMdwCmwQEAsDlG1gAAx2I1OAAAdhcn96yZBgcAwOYYWQMAnCtORtYkawCAc8VJsmYaHAAAm2NkDQBwrjh5zppkDQBwLB7dAgDA7rhnDQAA7MC+I2ufV3JdOL9L9Pq3D41jtv+beTvbNcw4xnXlD80bknQix7xIhOdwm3HM0SHm7aTtajGOkaSEtk7jGN9/fBZSW+aOdVM7ktRsHNERhV5ESnKIcf0j2otz+aLbWrrg+CzJFcbo2OeMkbV9kzUAAOfDNDgAALADRtYAAAcLc2StC3BkXVFRoSuvvFKpqanKysrSjBkzVFdXF3DMxIkT5XK5ArY777wzop0GAEDSt9Pg4WwOYJSsN2zYoJKSEm3evFnr169XR0eHJk+erJaWwMU8CxYs0P79+/3b8uXLI9ppAADiidE0eFVVVcDXa9asUVZWlrZu3arrr7/ev79nz57KyckJ6jPb2trU1vbtCuHmZvNVqACAOOWzFNZUtkNWg4e1wKypqUmSlJGREbD/ueeeU2ZmpkaNGqUlS5bo+PHjZ/2MiooKpaen+7e8vLxwugQAiCeWL/zNAUJeYObz+bRw4UJdc801GjVqlH//LbfcoiFDhig3N1effPKJFi9erLq6Or388stdfs6SJUtUVlbm/7q5uZmEDQDAaUJO1iUlJdq+fbs2btwYsP+OO+7w///o0aM1YMAATZo0Sbt27dJFF110xud4PB55PJ5QuwEAiGc8Z312paWleuONN/Tee+9p0KBB5zy2oKBAkrRz585QmgIA4Ox8VvibAxiNrC3L0t13361XXnlFNTU1Gjbs/K+2rK2tlSQNGDAgpA4CAHBWcTKyNkrWJSUlWrt2rV577TWlpqaqoaFBkpSenq4ePXpo165dWrt2raZNm6Z+/frpk08+0aJFi3T99ddrzJgxUfkDAABwoTNK1k8//bSkky8+Od2zzz6r22+/XcnJyXrnnXe0cuVKtbS0KC8vTzNnztT9998fsQ4DAOBnKcyRdcR6ElXG0+DnkpeXpw0bNoTVIXQ/68+fhhSXEuF+nE3aB93UkCRnPMQBwC9OpsEp5AEAgM1RyAMA4Fw+n8KaE/M5Yz6NZA0AcC6mwQEAgB0wsgYAOFecjKxJ1gAA56LqFgAAsANG1gAAx7Isn6wwylyGE9udSNYAAOeywizGwT1rAACizArznrVDkjX3rAEAsDlG1gAA5/L5JFcY9525Zw0AQJQxDQ4AAOyAkTUAwLEsn09WGNPgPLoFAEC0MQ0OAADsgJE1AMC5fJbkuvBH1iRrAIBzWZakcB7dckayZhocAACbY2QNAHAsy2fJCmMa3HLIyJpkDQBwLsun8KbBnfHoFtPgAADHsnxW2FsoKisrNXToUKWkpKigoEBbtmw55/EvvfSSRowYoZSUFI0ePVpvvfWWUXskawAADKxbt05lZWUqLy/Xtm3blJ+fr+LiYh04cKDL4z/44APNnj1b8+fP18cff6wZM2ZoxowZ2r59e9BtuiybTdg3NTWpT58+ulbTlKikWHcHAGCoUx3aqLd05MgRpaenR6WN5uZmpaenh50rTvW1vr5eaWlp/v0ej0cej6fLmIKCAl155ZV68sknJUk+n095eXm6++679bOf/eyM42fNmqWWlha98cYb/n1XXXWVxo4dq1WrVgXXUctm6uvrT72Oho2NjY3NwVt9fX3UcsWJEyesnJyciPSzd+/eZ+wrLy/vst22tjbL7XZbr7zySsD+OXPmWD/84Q+7jMnLy7N+/etfB+xbtmyZNWbMmKD/vLZbYJabm6v6+nqlpqbK5XIFfK+5uVl5eXln/AYUbzgPJ3EeTuI8nMR5OMkO58GyLB09elS5ublRayMlJUW7d+9We3t72J9lWdYZ+eZso+pDhw7J6/UqOzs7YH92drY+//zzLmMaGhq6PL6hoSHoPtouWSckJGjQoEHnPCYtLS2u/zGewnk4ifNwEufhJM7DSbE+D9Ga/j5dSkqKUlJSot6OHbDADACAIGVmZsrtdquxsTFgf2Njo3JycrqMycnJMTq+KyRrAACClJycrHHjxqm6utq/z+fzqbq6WoWFhV3GFBYWBhwvSevXrz/r8V2x3TT4uXg8HpWXl5/1XkK84DycxHk4ifNwEufhJM5D9JWVlWnu3LkaP368JkyYoJUrV6qlpUXz5s2TJM2ZM0cDBw5URUWFJOmee+7R9773PT3++OP6wQ9+oBdeeEEfffSRfvvb3wbdpu0e3QIAwO6efPJJPfbYY2poaNDYsWP1xBNPqKCgQJI0ceJEDR06VGvWrPEf/9JLL+n+++/Xnj17dMkll2j58uWaNm1a0O2RrAEAsDnuWQMAYHMkawAAbI5kDQCAzZGsAQCwOccka9NyZBeiBx54QC6XK2AbMWJErLsVde+//76mT5+u3NxcuVwuvfrqqwHftyxLy5Yt04ABA9SjRw8VFRVpx44dselsFJ3vPNx+++1nXB9TpkyJTWejpKKiQldeeaVSU1OVlZWlGTNmqK6uLuCY1tZWlZSUqF+/furdu7dmzpx5xgspnC6Y8zBx4sQzroc777wzRj1GuByRrE3LkV3IvvOd72j//v3+bePGjbHuUtS1tLQoPz9flZWVXX5/+fLleuKJJ7Rq1Sp9+OGH6tWrl4qLi9Xa2trNPY2u850HSZoyZUrA9fH88893Yw+jb8OGDSopKdHmzZu1fv16dXR0aPLkyWppafEfs2jRIr3++ut66aWXtGHDBu3bt0833XRTDHsdecGcB0lasGBBwPWwfPnyGPUYYQu65EcMTZgwwSopKfF/7fV6rdzcXKuioiKGvep+5eXlVn5+fqy7EVOSAqrd+Hw+Kycnx3rsscf8+44cOWJ5PB7r+eefj0EPu8c/ngfLsqy5c+daN954Y0z6EysHDhywJFkbNmywLOvk331SUpL10ksv+Y/57LPPLEnWpk2bYtXNqPvH82BZlvW9733Puueee2LXKUSU7UfW7e3t2rp1q4qKivz7EhISVFRUpE2bNsWwZ7GxY8cO5ebmavjw4br11lu1d+/eWHcppnbv3q2GhoaA6yM9PV0FBQVxeX3U1NQoKytLl112me666y4dPnw41l2KqqamJklSRkaGJGnr1q3q6OgIuB5GjBihwYMHX9DXwz+eh1Oee+45ZWZmatSoUVqyZImOHz8ei+4hAmz/utFQypFdqAoKCrRmzRpddtll2r9/vx588EFdd9112r59u1JTU2PdvZg4VWIu3PJzF4IpU6bopptu0rBhw7Rr1y79/Oc/19SpU7Vp0ya53e5Ydy/ifD6fFi5cqGuuuUajRo2SdPJ6SE5OVp8+fQKOvZCvh67OgyTdcsstGjJkiHJzc/XJJ59o8eLFqqur08svvxzD3iJUtk/W+NbUqVP9/z9mzBgVFBRoyJAhevHFFzV//vwY9gx2cPPNN/v/f/To0RozZowuuugi1dTUaNKkSTHsWXSUlJRo+/btcbFu41zOdh7uuOMO//+PHj1aAwYM0KRJk7Rr1y5ddNFF3d1NhMn20+ChlCOLF3369NGll16qnTt3xrorMXPqGuD6ONPw4cOVmZl5QV4fpaWleuONN/Tee+9p0KBB/v05OTlqb2/XkSNHAo6/UK+Hs52Hrpx6b/WFeD3EA9sn61DKkcWLY8eOadeuXRowYECsuxIzw4YNU05OTsD10dzcrA8//DDur4+vvvpKhw8fvqCuD8uyVFpaqldeeUXvvvuuhg0bFvD9cePGKSkpKeB6qKur0969ey+o6+F856ErtbW1knRBXQ/xxBHT4OcrRxYv7r33Xk2fPl1DhgzRvn37VF5eLrfbrdmzZ8e6a1F17NixgNHA7t27VVtbq4yMDA0ePFgLFy7UI488oksuuUTDhg3T0qVLlZubqxkzZsSu01FwrvOQkZGhBx98UDNnzlROTo527dql++67TxdffLGKi4tj2OvIKikp0dq1a/Xaa68pNTXVfx86PT1dPXr0UHp6uubPn6+ysjJlZGQoLS1Nd999twoLC3XVVVfFuPeRc77zsGvXLq1du1bTpk1Tv3799Mknn2jRokW6/vrrNWbMmBj3HiGJ9XL0YP3mN7+xBg8ebCUnJ1sTJkywNm/eHOsudbtZs2ZZAwYMsJKTk62BAwdas2bNsnbu3BnrbkXde++9Z0k6Y5s7d65lWScf31q6dKmVnZ1teTwea9KkSVZdXV1sOx0F5zoPx48ftyZPnmz179/fSkpKsoYMGWItWLDAamhoiHW3I6qrP78k69lnn/Ufc+LECeunP/2p1bdvX6tnz57Wj370I2v//v2x63QUnO887N2717r++uutjIwMy+PxWBdffLH1z//8z1ZTU1NsO46QUSITAACbs/09awAA4h3JGgAAmyNZAwBgcyRrAABsjmQNAIDNkawBALA5kjUAADZHsgYAwOZI1gAA2BzJGgAAmyNZAwBgc/8XpW9w9kcSpckAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对*训练集*和*测试集*进行预处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.832822Z",
     "iopub.status.busy": "2022-08-31T04:53:24.832266Z",
     "iopub.status.idle": "2022-08-31T04:53:24.977142Z",
     "shell.execute_reply": "2022-08-31T04:53:24.976296Z"
    },
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "为了验证数据的格式是否正确，以及您是否已准备好构建和训练网络，让我们显示*训练集*中的前 25 个图像，并在每个图像下方显示类名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:24.982349Z",
     "iopub.status.busy": "2022-08-31T04:53:24.981730Z",
     "iopub.status.idle": "2022-08-31T04:53:25.654800Z",
     "shell.execute_reply": "2022-08-31T04:53:25.654137Z"
    },
    "id": "oZTImqg_CaW1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xp+Y2NW6bN2/uxdwkXVV8VRViU8m97rU6ZBMOlD2pFpFWj3PHiOoTOpe6/dR3wPKGXzwAAAAAJI6FBwAAAIDEsfAAAAAAkDgWHgAAAAASd9BkTLkJPKpCr0ouHzp0qBfLy8uLtevVq+f1URXC1fHdpO1FixZ5fSpVquTFtm3b5sXcBDhVvVqdl6rU+pvf/CbWnjp1qtfHTQpVr2lZ5iYndu3a1eujNhb4+OOPvZg7/tT7pxL43dfYTCfoulSCpIq5x1LnpcafSuxVFeFRMtS4dGOpJluGPlaNt1Srp2dlZXkxEslLr+rVq3sxdf1zqblNjZmQyuUquXzlypVeLGRTFJUIj7JFzUepPi6kQr2ax9Q4dWMrVqzY31Msc/jFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHEHTXK5mwykKosr7du392JukqZK4g5NXncTiVTCpFv12kwnHbvnoZKVVcJd06ZNvZhbdfrmm2/2+hx33HGxtqqAXlaoBDL39VRjRiXZ1qhRw4u540GNhdBk35CqqUUptHK1qrzuCk24S/pvKu/U61daN38I2XgBpUPIxhZmOtnW3YRFzafq+qS4c406ltpIpUGDBl7MTTg/GCpHH4yKMrncvSaGVDc309/b3M1bcnNz9/MMyx5+8QAAAACQOBYeAAAAABLHwgMAAABA4hLP8VD3ual7jdV95O5jVSGzA7nnNMSZZ57pxTIyMmLtatWqeX1CCw65972qewBVsbaQHBX1N6vXS70f06dPj7VVAa/yRN2jqcabq1WrVl4sMzPTi6WaYxRy72hR5kOo8wodyyFjRH3OQwqLYf+E5HOEFmsLUZTHCh0jql/o9QCpCX3NVb7f2rVrY2113Vy9enXQebjXzc2bN3t91q9f78VC5l31N6rCvkqq3zOQvJAcj5DvoaHHDs2zc+c2cjwAAAAAoAiw8AAAAACQOBYeAAAAABLHwgMAAABA4oo8EyqkUFpJJGB9+umnsfawYcO8Pp999pkXq169uherU6dOrK0KXqnEIvV3u8dXyUfq+Crh3H3O0EJIKnnYfeybb77p9enbt2/Q8csqN9FMjWWVIOkWmDTz3y+VuK4KUYYkrak+IUWPFFXAUiVuquOTJF56hMwPoUWvQhK7D6Q4YchmCSqm5i01flF0QpP33eRvM7Mjjzwy1m7WrJnXR8016j1dvnx5rK2Sxps3bx50LDcRvlGjRl6fH374wYuh9JozZ44Xc+cLNaeEbojhzlmhxQlVP/d74apVq4KOVZbxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiijzLO9UE0zVr1nixpUuXxtoqYcjtY6YTod3HqgRglVikErTd6qrZ2dleH5XEppKH3SQ5dV4q4a5Hjx5ebOPGjbH2+PHjvT4qOVBVnHaTnz///HOvT3kXUhFcvZ4qlmpib8h5pZoQF/qcodXuQxJPi7LKOvYu5L0OrbYbevyiEnrsVCujI3nq2tOqVatYOzT5u0aNGl7MvdatW7fO66M2hlFJ6Oo7hMu9TpuZrVixwovVr18/1g6t9I6iNWvWLC/WpEmTWFuNBfVdS3Gvf6Fzlrpuut/5li1b5vWZOHGiF1PfAcsKPgEAAAAAEsfCAwAAAEDiWHgAAAAASBwLDwAAAACJK/Lk8kmTJsXagwcP9vqsXLnSi6nkMDcJSyVq1axZ04upBHc3QU0lcasEIVWZ2k3qGTp0qNfn2GOP9WJuhVQzP5kuNzfX66NMnz7di+Xn58fabjKVmU6WVwlVmzZtSum8oJMV3XEaWuk51YTwVKljqyrrqt/OnTsTOSfsvwOpJB7CTUI/kMq97rHUOFJ/D+Mtee41VyVGL1682It99913Xqxly5ax9tq1a70+7sYtZmatW7f2Yu716fvvv/f61KpVy4upa3CIjIwML/bKK694sRtvvDHWJpG8ZHz00UdeLGRTltDNANx5LHRTDnV897FqvD/99NNejORyAAAAANgHFh4AAAAAEsfCAwAAAEDiDjjHY9euXbH7bwcNGhT7d3W/e8WK/tOq++hUPoJr27ZtXkzlZaiYa/369V5s4cKFXuy2224r9NjqnrxGjRp5MTfH45RTTvH6uIWXzMzmzp3rxdz7Y9W9+eq+aHXfofseuYWRDgapFrsLKaK5fft2LxZyL6mKhRaCC+mnzkvlQ6njh9xzTwHB4qHea3dcho6RkEJ9oe+r6hdyfHVear7OzMwMOg+ECclRGD16tBc74ogjvNjWrVtjbfVeqett48aNvdjs2bNjbTXnqhxHlRvZoEGDWFvlmah8kR9++MGLudflww47zOuD5KmCx+53GnW9OpBCgCHUXOd+LtT1VhUQLMv4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB5xc/sorr8SSq93kMLdokJlf/MfMbOPGjV5MJXm5VIKQSjp0E81UwtqWLVu8mJt4ZmY2cODAWPutt97y+vTt29eLLViwwIu5r8VXX33l9Rk7dqwXU8lNblKSSrxXycOKm4ilHucWjlLv4cFIJYe5SWUqGTK0eFFIwTW1sYBK7HXHkeqjNoNQVBFQlIwdO3Z4MXd8FWXRv6Kkxpt6PjcpEyVDJWx37NjRi7njT11T1DVLCdnIImTuNPM3eFEFEVUifEhyPMnlJUMVPHY3CDiQeS3kuhnK/Vyo76HLli3zYuqzor57lEb84gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4A04ur1evnlWvXr2g7SZxq4RjlQDTrFkzL+Y+ViVMbtiwwYvVrl3bizVv3rzQ83KTzPYWcxOD+/fv7/Xp0KGDF1MJT24CvXptatas6cVU8rB7XpUrV/b6pFoxWyVizZkzJ9ZWmwYcjEIqlyupJrupjQZCE8Ld44eegxp/Kiku5FgoeiFVedW4KYn3J2SsqvEWmhyPoqM2SGnUqJEXU4n/GRkZsbYao2ruDJlX1BhS17WQ5PWffp/ZQyX3qg1qVq5cWejxUbTWrl3rxdT7UL9+/VhbjQU1ZtSmL+48GfIdam8x9zxOP/10r89///tfL6Y2IurRo4cXK434xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB5xcnp2dHUsac5NsmjZt6j1GJSGrZCA3qbpevXpeHxVTSWtuAo/qoxLi8vPzvZiblFmnTh2vz3fffefF3OQ6Mz+p3q2uubfzUn+3m2CnEjJVEl5IpcysrCyvz9SpUws9z4ORStoNkWpi74Ek2brPGZJIZ6aTQDdv3pzyeaBoqU0kXOp9DU2uTFLoZgZsZlH8VFVvNWbU9dUdk+p6oa5PalMZl0owVsdSc7N7roceeqjXZ+7cuUHHWr9+fay9Zs0ar4/a/Aap++abb4L6ueNBfe8Jnf/csavmW3WNDJnbcnJyvD5qrM2aNcuLkVwOAAAAAP8/Fh4AAAAAEsfCAwAAAEDiDjjHo2PHjpaZmVnQdovpvfDCC95jsrOzvVirVq28mFu8T+VbqHvr1L177n2i6h5UVSxQ9XPv01MFh1RRJXX/oHsfoHo+VUAwpDCjepyKqUKD7n2HqnBUgwYNYu2QQk9lSVEWUyvK++RDcjpC80xCCgiqcw+9fxolQ82L7nut3sOSKMrnji91b7TK8Zg/f74X69SpU9GdGDzq+qTmB3VNdHPAVO6Guhap8eBeS9X1UI1vVaD3hx9+iLW7dOni9fn000+9mLrGu6+Pyj0hx6NojRw50ovVrVvXi7lzSMi4MtPfO915Un0u1ON++l15D3ecqmKV6lxnzJjhxcoKfvEAAAAAkDgWHgAAAAASx8IDAAAAQOJYeAAAAABI3AEnl7vuuOOOWPvoo4/2+vz1r3/1Yip52S2SpxKjVRKbSnZzCwiGFBIy08mWbpJmSLEkM5187T42NLlT9XNfC5VwpwoaqYQqN8GpY8eOXp9f/epXsfaGDRvsqquu0idcBrmvcWiyuUqQTDXxPqSgkUo8U58BdSyX+hvVWFPPGZJcXpQJ+9i7pUuXFtontFikGjfuex36voaMSzXeVKKwSiBFslavXu3F1LVOFbidOXNmrK3mRFWoVh3fHQ+hG8+oDWSmT58ea5999tleH/XdQx3fTSZX3w1QtNQmE+q7j/udRl2vVDFolez9zjvvxNrnnHOO16datWpeTBXZVYWlQx737bffFvq40opfPAAAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABJ3wMnlu3fvjiUMuomCZ511lvcYFfv444+9mJuonpub6/VZv369F1PJiW4ikaqaGlrJt379+rG2Sqxs0qSJF1OJbW5i0YFUf3aTmkMT73v37u3F2rVrF2v36NEj5fNCWEJ4aNVwNxaaSB6ycYEay6FV16lcXnqoucad89R7rd7DkM0FQt97VYHcfWxo9eBmzZoFPSeKzsqVK72Ymh9Uku66detibTVmsrOzvZhK4q5Vq1asnZ6eHnReIVSyr/t8Zvrz455HXl6e16dt27YpnRc0ldg9btw4L+bOY2qeUUncSkhCuPo+qea/kMep+bxDhw6FHqu04hcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxB1wcnmFChWCKiIX5pRTTvFin3/+eaGPmz17thdTCXBuctiSJUu8Ps2bN/diqgp1q1atCj0vlH2pVtlWCZJz586NtVUCmfocqZibbKn6qHNXMfc81KYLoahcXnp07drVi82ZMyfWdpN9zXQSo+ImaqrxnOp7rRJy1RgnSbf4bdq0yYupTUzcCt7K1q1bvZi63qrq3+41XlVKV+eqvhu4MVUJO3SzDnfMqwraKFpXXnmlF7vqqqu8mPt+qQ0Q1KYvSsh33rp163oxNee6Y37Dhg1eHxUbNGhQoedQWvGLBwAAAIDEsfAAAAAAkDgWHgAAAAASd8A5HiXt8MMPD4q52rdvn8TpAPI+TrcAmsqlWL16tRdTeRNuYawDyctw781Xz6eKYW7ZssWLqXujXaHFDnFg1H33l156aaw9duxYr8+qVau8mLpX3r3vPqQwlpkeX+4YbNGihddH5QCqvxHJcnPVzMwOPfRQL6byN1xqLlAF3FTekVvQ9pVXXvH6qNyQU089tdDzUOel5nQ1/lq2bBlrn3zyyV4fJG/69OlerGPHjoU+rkqVKkHHX7FiRaF9li1b5sXU58KdE1Ve0OjRo72YykkuK7jiAwAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJK7MJ5cDSXELDoUWROvcubMXO/LII2PtmjVren1Ck8Td5MeMjAyvjzpXVfDKTexVid4qcVglW6qidS4SyYuHeq/dJN0zzzwz6Fhr1qzxYm7i5Pr1670+agw2bNiw0FiqRQz39pwoOk899ZQXU8UjVYL2RRddFGurzShUwuzixYu9mJvQ3qVLF/9kA51//vmF9rnwwgtTPj6KX4cOHbyYO1+MHz/e6zNr1iwv9vHHH3uxnj17FnoO1113nRdTSenu5+Kss84q9NhlHd8CAAAAACSOhQcAAACAxLHwAAAAAJC4lHM89twvt2HDhiI7GZRte8aCuve6qBXH+Es1x2Pbtm1ebPv27YX2STXHQ91jXZQ5Hqromzp/t/hXcc8NxTn+fvo8pXEOLMr8B/X3uQUxVZFB9XyqOJZbiM39rOxNacvxOBjGnyrKF5rj4c4taiyovyW0H8rfNThJas5ShXHVddkdk+np6V6fkO8BZiV/3SxKoeMv5YXHnhe+adOmqR4C5dTGjRstKysr8ecwY/zBVxzjb8/zmDEGEcf4Q0njGpysN998s8iO9dprrxXZsUqLwsZfWpTi0nj37t22dOlSq1GjBjuJwMx+XOVu3LjRsrOzE9+9iPEHV3GOPzPGIOIYfyhpXINRkkLHX8oLDwAAAAAIRXI5AAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJO6gXnjcc889dvTRR+/134cMGWI1a9Y8oOe47LLLrF+/fgd0DJR/hY1FM7OTTjrJbrzxxmI5Hxx8GIMADlbMf8WnTC88Jk2aZIcccoidffbZJX0qJY4PRPFKS0vb5//uueeeIn/ON9980+6///599snNzbW0tDSbOnWq/Pd7773XfvWrX5nZj3/DW2+9VcRnieLCGERZd9lllxWM10qVKlmDBg2sd+/e9vzzz9vu3btL+vRQijH/lV0VS/oEDsRzzz1n119/vT333HO2dOlSy87OLulTwkEiLy+v4P8PHTrUBg8ebDk5OQWxjIyMIn/O2rVr7/Pft2/fXugx3n77bbvtttuK6pRQghiDKA/69OljL7zwgu3atcuWL19uo0aNskGDBtkbb7xhI0aMsIoV/a8pO3bssEqVKpXA2aK0YP4ru8rsLx75+fk2dOhQu+aaa+zss8+2IUOGxP593LhxlpaWZh999JF16dLFqlevbj169IgNTNf8+fOtZcuWdt1111kURbLP22+/bZ07d7aqVatay5Yt7d5777WdO3cWer733nuv1atXzzIzM+3qq6+ODdBt27bZDTfcYPXr17eqVatar169bMqUKbHHf/LJJ9a1a1erUqWKNWrUyG677baC573sssvsk08+sccff7xgtZ+bm1voOSF1DRs2LPhfVlaWpaWlxWJq0hs3bpx17drV0tPTrWbNmtazZ09buHBhrM9LL71kLVq0sKysLPvFL35hGzduLPg391etFi1a2P3332+XXnqpZWZm2lVXXWWHHnqomZl16tTJ0tLS7KSTTirov3jxYvv222+tT58+1qJFCzMz69+/v6WlpRW0zcyefvppa9WqlVWuXNnatm1rL730Uuwc09LS7Omnn7YzzzzTqlWrZi1btrQ33ngjxVcSqWIMMgbLgypVqljDhg2tcePG1rlzZ7vjjjvs7bfftvfff7/gur7n/T733HMtPT3dHnzwQTPb9/U4iiK75557rFmzZlalShXLzs62G264oeB5n3rqKTvssMOsatWq1qBBA7vggguK/W9H6pj/yvD8F5VRzz33XNSlS5coiqLonXfeiVq1ahXt3r274N/Hjh0bmVnUrVu3aNy4cdG3334bHX/88VGPHj0K+tx9993RUUcdFUVRFE2bNi1q2LBh9H//938F//7CCy9EWVlZBe1PP/00yszMjIYMGRLNnz8/+uCDD6IWLVpE99xzz17Pc+DAgVFGRkZ00UUXRTNnzoxGjhwZ1atXL7rjjjsK+txwww1RdnZ29N5770XffvttNHDgwKhWrVrR6tWroyiKoiVLlkTVq1ePrr322mjWrFnR8OHDo7p160Z33313FEVRtG7duqh79+7RlVdeGeXl5UV5eXnRzp07U35tsX/ccaLs2LEjysrKin7/+99H8+bNi7777rtoyJAh0cKFC6Mo+nEsZmRkRD/72c+iGTNmRJ9++mnUsGHD2Dg58cQTo0GDBhW0mzdvHmVmZkZ//etfo3nz5kXz5s2Lvvjii8jMojFjxkR5eXkFYyiKoujvf/97dPrpp0dRFEUrVqyIzCx64YUXory8vGjFihVRFEXRm2++GVWqVCl68skno5ycnOjhhx+ODjnkkOjjjz8uOI6ZRXXq1ImeeeaZKCcnJ7rzzjujQw45JPruu+8O9KVEihiDjMGyaODAgdF5550n/+2oo46KzjzzzCiKfny/69evHz3//PPR/Pnzo4ULFxZ6PX799dejzMzM6L333osWLlwYTZ48OfrXv/4VRVEUTZkyJTrkkEOiV155JcrNzY2+/vrr6PHHHy+WvxlFj/mvbM1/ZXbh0aNHj+ixxx6LoujHAVW3bt1o7NixBf++Z+ExZsyYgti7774bmVm0ZcuWKIr+38JjwoQJUa1ataK//vWvsedwB/Opp54a/eEPf4j1eemll6JGjRrt9TwHDhwY1a5dO9q0aVNB7Omnn44yMjKiXbt2Rfn5+VGlSpWil19+ueDft2/fHmVnZ0cPPfRQFEVRdMcdd0Rt27aNLayefPLJgmNEkf+BQPEJmfRWr14dmVk0btw4+e933313VL169WjDhg0FsZtvvjnq1q1bQVtNev369YsdZ8GCBZGZRd988433HL17947+/ve/F7TNLBo+fHisT48ePaIrr7wyFrvwwgujs846K/a4q6++OtanW7du0TXXXCP/NiSPMcgYLIv2tfC46KKLonbt2kVR9OP7feONN8b+vbDr8cMPPxy1adMm2r59u3fsYcOGRZmZmbGxjrKL+a9szX9l8larnJwc++KLL2zAgAFmZlaxYkW76KKL7LnnnvP6duzYseD/N2rUyMzMVqxYURBbtGiR9e7d2wYPHmy/+93v9vm806ZNs/vuu88yMjIK/nfllVdaXl6ebd68ea+PO+qoo6x69eoF7e7du1t+fr4tXrzY5s+fbzt27LCePXsW/HulSpWsa9euNmvWLDMzmzVrlnXv3t3S0tIK+vTs2dPy8/NtyZIl+zxnFL9FixbFxsgf/vAHq127tl122WV2xhlnWN++fe3xxx+P3aNq9uPPtjVq1ChoN2rUKDZWlS5dugSd04YNG+yTTz6xc889d5/9Zs2aFRuLZj+OtT1jcY/u3bt7bbcPSg5jEGVdFEWxa547zgq7Hl944YW2ZcsWa9mypV155ZU2fPjwgtuwevfubc2bN7eWLVvaJZdcYi+//PI+r+EoW5j/SrcyufB47rnnbOfOnZadnW0VK1a0ihUr2tNPP23Dhg2z9evXx/r+NAFtzyT2090y6tWrZ127drVXX33VNmzYsM/nzc/Pt3vvvdemTp1a8L8ZM2bY3LlzrWrVqkX4F6Isy87Ojo2Rq6++2szMXnjhBZs0aZL16NHDhg4dam3atLHPP/+84HFusmRaWlqhO7ukp6cHndP7779vRxxxhDVt2nQ//xqURYxBlHWzZs0quF/ezB9nhV2PmzZtajk5OfbUU09ZtWrV7Nprr7UTTjjBduzYYTVq1LCvv/7aXn31VWvUqJENHjzYjjrqKFu3bl0x/5VIAvNf6VbmFh47d+60F1980R5++OHYwJo2bZplZ2fbq6++ul/Hq1atmo0cOdKqVq1qZ5xxRiyRyNW5c2fLycmx1q1be/+rUGHvL+W0adNsy5YtBe3PP//cMjIyrGnTpgUJRBMmTCj49x07dtiUKVPsiCOOMDOzdu3a2aRJk2IJ7xMmTLAaNWpYkyZNzMyscuXKtmvXrv3625GMihUrxsbGT3fC6NSpk91+++02ceJEa9++vb3yyitF+tyVK1c2M/PGwttvv23nnXdeLFapUiWvX7t27WJj0ezHsbZnLO7x08l6T7tdu3YHdO4oOoxBlGUff/yxzZgxw84///y99gm5HlerVs369u1rTzzxhI0bN84mTZpkM2bMMLMfPyOnnXaaPfTQQzZ9+nTLzc21jz/+uFj+PiSL+a90K3Pb6Y4cOdLWrl1rV1xxhWVlZcX+7fzzz7fnnnuuYHUbKj093d59910788wz7cwzz7RRo0bJHREGDx5s55xzjjVr1swuuOACq1Chgk2bNs1mzpxpDzzwwF6Pv337drviiivszjvvtNzcXLv77rvtuuuuswoVKlh6erpdc801dvPNN1vt2rWtWbNm9tBDD9nmzZvtiiuuMDOza6+91h577DG7/vrr7brrrrOcnBy7++677aabbiqYYFu0aGGTJ0+23Nxcy8jIsNq1a+9zMYTitWDBAvvXv/5l5557rmVnZ1tOTo7NnTvXLr300iJ9nvr161u1atVs1KhR1qRJE6tataqlp6fb+++/b7///e9jfVu0aGEfffSR9ezZ06pUqWK1atWym2++2X7+859bp06d7LTTTrN33nnH3nzzTRszZkzssa+//rp16dLFevXqZS+//LJ98cUX8lZHlB6MQZRG27Zts2XLlsW20/3jH/9o55xzzj7HZmHX4yFDhtiuXbusW7duVr16dfvPf/5j1apVs+bNm9vIkSPt+++/txNOOMFq1apl7733nu3evdvatm1bjH85ihPzXylS0kkm++ucc86JJdn81OTJkyMzi6ZNm1aQXL527dqCf//mm28iM4sWLFgQRVF8V6soiqKNGzdGPXr0iE444YQoPz9fJiyNGjUq6tGjR1StWrUoMzMz6tq1a8FOGcqe5LnBgwdHderUiTIyMqIrr7wy2rp1a0GfLVu2RNdff31Ut27dqEqVKlHPnj2jL774InaccePGRccee2xUuXLlqGHDhtGtt94a7dixo+Dfc3JyouOOOy6qVq1a7G9E8kIS25YtWxb169cvatSoUVS5cuWoefPm0eDBgws2B3DHYhRF0aOPPho1b968oK0S2x599FHvuZ555pmoadOmUYUKFaITTzwxGjNmTNSkSROv34gRI6LWrVtHFStWjD3PU089FbVs2TKqVKlS1KZNm+jFF1+MPc7MoieffDLq3bt3VKVKlahFixbR0KFD9/n3I1mMQcZgWTRw4MDIzCIziypWrBjVq1cvOu2006Lnn3++YFxGkU7CjaJ9X4+HDx8edevWLcrMzIzS09Oj4447rmCzmfHjx0cnnnhiVKtWrahatWpRx44dGT9lGPNf2Zr/0qJoLwUrAJQLN9xwg+3cudOeeuqpIjleWlqaDR8+3Pr161ckx0P5xxgEcLBi/osrc7daAdg/7du393bAAIoTYxDAwYr5L46FB1DOXXXVVSV9CjjIMQYBHKyY/+K41QoAAABA4lL+xWP37t22dOlSq1GjRqzIDw5eURTZxo0bLTs7O/EdtRh/cBXn+DNjDCKO8YeSxjUYJSl0/KW88Fi6dCmFUCAtXry4oL5IUhh/2JviGH9mjEFojD+UNK7BKEmFjb+UFx57ysovXrzYMjMzUz1MAXXHV1GuoleuXBlrf/LJJ16ff//7317MrRViZt5e33sKxvyUqoD6xRdfeLFjjz021r777ru9PtWqVfNiIZJ+TV0bNmywpk2bFoyNJBX1+EPZV5zjz6x4xmDInbBF+Zn+7LPPvNhPq0fv0bhx45SOn5ub68W++eabWLt///4pHbuklcfxh7KFazBKUuj4S3nhsedil5mZWSYWHlu3bo21q1ev7vWpWNF/OSpVquTFqlSpss/23mLq+G4/9VqWlYVHSTxHUY0/lB/F9bN/cYzB4l54pKenezF1EUn171XHcufisv55Lk/jD2UT12CUpMLGH6WtAQAAACSOhQcAAACAxJVIHY9UbwFatWqVF3v88ce92JgxY7yYe6uVuqVg+/btXmzKlCle7M0339zneZrpW7TUfdGTJ0+OtXv06OH1qV27thc78cQTvdj1118fa9eqVavQ8wRQerlzZehONUuWLPFizz//fKz98MMPe302bNiwH2dXNNy/6ZJLLvH6/PnPf/ZigwYNSun5du/eXeg5AACSwWwLAAAAIHEsPAAAAAAkjoUHAAAAgMSVSI5HqPnz58fa55xzjtenYcOGXqxmzZpezM25OOSQQ7w+agvcLl26eLH8/PyUjqVySNz6Ijt37vT6bNu2zYt9+OGHXmzChAmx9q9//Wuvz89+9jMvBqDkpZp70KlTJy82d+5cL+bOI2pLcTWfuvlxZn7+mJpz8/LyvNiWLVu8mLtduHq+3//+917sD3/4gxc79dRTY+1XXnnF66NeU/I+Si+VE+q+X+q9Ct1Stri3rJ44caIXU7mdOTk5sXabNm0SPS+EK+4xk6pf/epXXuymm27yYp07d/Zi7vVCfadNFTMrAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kokuTw06eb222+PtRs1auT1UUXyVIK2+5wVK/p/ukoYchPJzfwkm9BE8k2bNnkxN+ldnVfVqlW9mEqGdJ/zySef9PqcfvrpXiwjI8OLAUiOmmtCkpm7d+/uxWbOnOnFGjRo4MXc+UHNw2reUnPSsmXLYm2VSO4mjZuZVa5c2Yu5yeRqvlMxNc+/+uqrsfbmzZu9Pm+99ZYXU6+9+x6VhmRRaAfy3hTl+zpu3LhYe8aMGV4ftfHDHXfc4cXc8ffBBx94fYoy4bc8SbVIdejj3Jh6XKrnsGPHDi+mClKrsXXBBRfE2nPmzPH6qO+0ak5Mcr7jFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrK5So50U1gzMzM9PqoRByVDOkmGapE7127dnkxVZXcjanERFV9VyU6uo9VSUTqHFRCuJuAqf7GESNGeLFf/vKXXgxAckIT94YPHx5rf/75516fpk2bejG1+YQ7V4YkTe4t5s7FIdWl99bPnQPV3KnOQc2VzZo1i7VHjx7t9Xn//fe92Jlnnhn0nEhdqsn6qp+6JoZ48cUXvdhxxx0Xa48fP97r88QTT3ix7OxsLzZt2rRYW1UbV1WiH3vsMS929NFHezGEUWMm1Wrj6nuhS811avMLteGG+1g1r3366aderH///l7M3bzj8MMP9/qoTYcUdR5FhV88AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqksvXrl3rxdzkcpVQtm3bNi+mkrjdx6oKvSHVa838ZCCVkKQSi5SQCpgqWX7lypVerG7durG2+hvHjBnjxUguB5ITummF8rOf/SzWdj/jZmYbN270YjVr1vRibrKg2pgjdC5z+4VUXd+bkMeGzs3unKdeh7POOsuLqc1NGjZsGGur10HNzSh+s2bN8mLq/XIri5uZffnll7H2mjVrvD4DBw70YieeeKIXcxPH3WPvLeYmBZuZzZs3L9Zu3bq11wfhUt0sImSuVn1Ck7PduW3x4sVeHzVn1ahRw4u515qHH37Y69O4cWMvlmqV9VTxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEldqblCdPn26F3Pv0XRzPsx04RYVc4vrqeI/rVq18mItWrTwYtWrV4+1VVGY9PR0L6bu+XNzVGbMmOH1eeedd7yYes5169bF2vn5+V4fVVQQQHJC8znOO+88L+bmKKjCobm5uYU+ziysWKkSUkCrKKl8jtAicu7c787VZv61wEzf+/+LX/yi0OdDuFTvGVc5mxMnToy13XwcM7OsrCwvdvnll3uxRx99NNZW98DfdNNNXmzFihVezP0bVQG3r7/+2ot9+OGHXswdp+R4HBh3bjiQvLTly5fH2iovaPXq1V7sq6++KvRYKjepdu3aXkyN+fXr18faXbp08fqUBvziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkLhSk1zuJvKZmR1//PGx9ssvv+z1mTlzphe74447vJhK8gqhEtu2bNmyz7aZTuLeunWrF3OT0FUxvz/+8Y9e7Nhjj/VibvK9Sqz8/vvvvRiAkjdp0qRC+6iCqUpI4qRK9g1NAFYFp4pK6Hmpc3D/blUkUc3DU6ZM8WLuNSnJgloHA3eTgtBNBNQmKVWqVIm11fcAtWHAP//5Ty82atSoWPuMM87w+ij169cvtI9KQFeJwj/88IMXe/7552Ptnj17en3at29f6DngRyHjb/78+V7sxhtv9GLuRj6qmN+3337rxdSmRt99912sfdJJJ3l91IYH6lrgfi5CC1mnyn1NQzci4RcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxJWa5PJbbrnFi7nJPyeffLLXp1OnTl5sw4YNXsxNLleJiZmZmV6sTp06XsytCqwqAIcmQ7qVJlWSnKpYqhLt3arG6tzd5CMUj5BkXDVmVLKW+7lQj1NJZRUrpvZxdyu+qnM4ECoB2D3XgyGxt1q1al5s+/btsXboe6jGmztPhbzuZmEJgyFVxPd2XiHHUtQYd6s9qwRMd0MPM7NXXnnFiz388MNB54EwIfOWoj4X7jj6+OOPvT6/+tWvvNg//vGPoOcsKqp6tfp+cswxx3ixypUrx9pqLLvH37hx4/6e4kFDfU9ztWrVyosNGTLEi6nvVkWlXr16XkxtiKE2FrjoootibZXMHvKdQvVTc7d7vQidu/nFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHGlJrlcVQv96KOPYu1hw4Z5fT744AMvNnDgQC/21FNPxdpuUreZ2bx587yYqprqJsWpJEeVuOkmi5n5ST0qIU5VxfzTn/7kxdzE8Vq1anl93nzzTS82ceJEL6aqqyJ1qSZHq4SukGOlmkjufk7MzB544AEvtnTp0pSOr4Qk/ZU306ZN82IrV670YllZWbG2SjJU84rq5yZeqyTD0CRxt9+BVBt3+6k+6hzUGHcfu3btWq+P2mAj1c8LwqU6B6rr3wknnLDP9t5s2bLFi7mfi9DzDBnLeXl5Xh91XVYb25x55pmFHmvhwoWxtvq+ggOjEsnd+UjNpale19QmSuq7rxpHn3zySax96623en1CE8BD+qW6uQG/eAAAAABIHAsPAAAAAIlj4QEAAAAgcaXmxtbbbrvNi7n33apiKO3atfNiI0aM8GL33Xdfoeeg7slT9wOH3N+s7hkOyQXZtGmT18ctWGhm1q1bNy/WsGHDWFvdK6iKEZLPUfxCczdSvfdcFUSbOnWqF3v99ddjbfd+ZzNd0GjAgAFe7NVXX92PM/x/3CJ5ZmYPPfRQrH3nnXemdOzSSs0FKo/Bpe7hVsWf1PhynzM0l0L1c+9pVucQeqyQe4lDH+eel5rT1bkuWbKk0HNA6ZHq+FPcfqH3wIdQeVtuoV+zsM+i+uy71wc1r+DAhFyrQ/M5Qgr7XnrppV4f9zq9t/Ny85RVTpMqyKl89913sfZvfvMbr0/jxo1jbZXbrPCLBwAAAIDEsfAAAAAAkDgWHgAAAAASx8IDAAAAQOJKTXJ5//79vZhbQPCrr77y+rhFdszMzj33XC+2YsWKWLtZs2ZeH1UERiXLuAk76nGKShSuXr16rK2SlFRRFrdwkJnZo48+WmifcePGebFOnToFxRAmJBkttEjV3LlzvZibaDZp0iSvjyqs2bJlSy/WpEmTWFsV68rNzfVi7733nhdL1WuvvebFJk+eXGTHL42+/vprL6aS7EOK66kCgiqB0N24IjQhUo1VN5k3pI+ZnitDCrKGzrFuP5VcqTZLUAm/7hhUG3qgZIQkgKs+6nMRMrZSLeSqNov597//7cXOOeccL/bLX/4y1lZj1P17Qj8nCJdq4UtFzYkuNRZUscB169Z5MbcQpfsd2sysadOmXkx9/3apYqzuJjYbN260//73v4Uei188AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqkstnzZrlxdzEa7cyt5nZcccd58UmTJjgxWbMmBFrq4Sh0EqnIYnCKhlNCanAqv5uN/HMzOzoo4+OtQ899FCvj0osatu2bWGnWa6o91m97m6yr0riVUKS0VRi2B133OHFhg4d6sXS09Nj7UaNGnl9unbt6sXURgmbN2+OtQ8//HCvzw8//ODF7rrrLi/mcjd0MNN/z0033eTFZs+eHWurjSWOOeaYQs+htFLzQ0h17tCE8JDnVMfaunVroedg5s9bBzIHutSxtm3b5sWysrK8mFvdWSWqq79bHf+xxx6LtV999VWvD1JPvC4t3PEdmqAdkuBep04dL6Y2bvnyyy+92K9//etYe/78+V6fHj16xNoklx+YVMdy6Hye6udCfW9Tmw6tWbMm1u7bt2/Q8Rs0aODF3Hny5JNP9vq43z3c7yZ7wy8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSs1yeUqccpNlFq8eLHXRyVeu0npZn6ijKoCqpKBVLXxkITw0GRLN7lXJT6qJF31N7qJlSopWCU1L1u2zIupKtdlUWjSlxKaTO5S1UKHDRsWa7sVP83Mateu7cWOPPJIL+aOyfXr13t9NmzY4MVU1V43GUwlOarP2Msvv+zF/vKXvxT6fB06dPBiKrHXTXJWFdXLMjX/KO5co+YHNU7VGE818TR0041Uueeq/h41b6k51t0QombNml4f9feo51SJ9vCVpUTyECFJ43szderUWPuoo47y+gwYMMCLjRw50ouNHj061nbHtpmfdKzmfYQr7irloaZNm+bFOnbs6MXy8vJi7ddee83ro8bI4MGDvZj7fbJ3796FnmcofvEAAAAAkDgWHgAAAAASx8IDAAAAQOJKTY6Huie5atWqsbbKt1D3frt5E2b+/XbqPl91D7Q6L/ex6l4+9TjVzz2Wuo9TnWvdunW9mMstJmOmC2otXbrUi5WXHA91z2aq9/A+8cQTXuzpp5/2YsuXL/di7r247du39/qo8a2O5VJ/Y2iOkTsm69Wr5/UJvW/YLWY1fPjwoMc98MADXuzJJ5+MtZs3b+71+c9//hNrq4JKpdUf/vAHL6byN9yYyodRn3NVuCzVgn5Jc+ddlW+hPrPqtXCLZKpcGnV9UDlzb731Vqxd1gvlQXPHX+j14c9//rMXcz+LV199tdfnpZde8mLq83rWWWfF2rm5uV4f97OSal4i9o87F6h5QH3XUmPLfayaZ6pUqeLF1HffVOf4Bx980Iu53zsvvPDClI6t8IsHAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kpNcrlKoHYTZVRydq1atbzYli1bvFhIcnlooqDbLzSRVyWPugmSKiFJnWuDBg28mJuMrxKZ1PHLUlJuYb7++utY+8MPP/T65OTkeDFVLMxNulevkypQ1qRJEy/mFvlTibGqEKDiJsKq9zR0cwM3GVf1UYUA3bFmZjZ58uRYu1GjRl6fTZs2ebHGjRt7sTZt2sTaKiH4mWeeibXVa1paff/9915MJRC6f5PafEIl3qvXq7Qml7tC5071eXTHs5qbQzcWadGiRaHHQtnnXidVEvc999zjxdS8W79+/VjbLRxrZnbYYYd5MXfcmvnXn4MxcdydC0K+J+6Ne20rygJ/Ic9nFjaHdOnSxYudfPLJXswtMBlKXUPU/OdeV0I2NArFLx4AAAAAEsfCAwAAAEDiWHgAAAAASBwLDwAAAACJKzXJ5YqbvKUScxo2bOjFVGJliNCEXPe8VMJTaMxNbFNJPopKRA1JxFLVsUOfszT65z//GUt+fvPNN2P/rjYaUO+zStxzE/7S09ODjpWfn+/F3HGkKiWrRHWVwOh+DlRivDovlXztjhH1eqnjqwS1rKysWFttbqA2g1CJw+55lOUNEH744Qcvpl5nlbznzmXqtVJzlPpMu/1CK3Gr91G9/yHUubrHD63cqzZjcD/HahMENZbUvLho0SIvVp6pMRNaxbu4ueeqxowao2renTVrVqx98803e33czS7MzBYvXuzFHn744Vg7dEOCqVOnejF3A4ru3bsHHaukhVT1Dp173FhpHY9KaPL6z372s1i7Y8eOXp8XXngh6Fju9Tzk+6uZ3vSlU6dOQc+ZCn7xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFypSS5PtSqsSlZVyTMulXSjEp5U0qGbwBOSFLU37vFVgp86L5Wc6iYnh1ZyVsnDZcUvfvELy8zMLGgfe+yxsX+fMGGC95iZM2d6sYULF3oxNwl17dq1Xh9VcTZkzKxYscLrs2rVKi8WkjiskijVeYVUeM3IyPBiKqleJeO7iX/qM6CSfUOSQFVy8dlnnx1rb9q0yR5//HGvX0kbP358UL+QJG6VXK5e0zVr1ngx9z0LTSQPmcuSruqt3n81Lt3Pi9roQV0f1GuoNucoz0ISd0OrRCc9HkI2ZVGJ5Gqjh0ceeSTWPuWUU7w+kydP9mKvv/56oecZSr1e7t+k/p7SyP1bQhPJUzV79mwv9vzzz3sxd9OAevXqBR1fzQPuPKO+Q6k55c477/RiK1eujLXdDXL2R0hCu+qj/sZWrVoVeiz3vQ2uIh/UCwAAAAAOAAsPAAAAAIlj4QEAAAAgcaUmx6MoqfvtQopUhRb9c4Xerxhyb526l3ndunVeTOV4HHbYYbG2Kkqk7s0PvS+vNIqiKHb+7du3j/17t27dgo6j8mEWLFgQa8+bN8/rk5ub68WWLl3qxdwxGTr+1JipU6dOrF2jRo1C+5jpAoVu0T/VR91bHHK/scrdCB1rbjE9dT+/+7nbsGFD0LGLm8rLUNRn3x0T6vVT84O6593NPQodbyHzovobQ99r91zVfBqa2+L2U3lZIa8NtKRzN5SQHIHQwnL33HOPF8vOzo61p0+f7vUZOnRo0PFTpT53bs6funaXBjt27IjlFLrvl/rb1OdN5T88++yzsbYqGK24124zs7fffjvWzsnJCTpWSD6wmotUgUmVF/Tee+8Veg7q+95PCyfvEVJAUM2J6nPdq1evQs+LHA8AAAAApRYLDwAAAACJY+EBAAAAIHEsPAAAAAAkrtRk1KkEWbf4U2hBJ5WI4yYdqmS0kOIrZmEFclQspLBhaPK3ei2aNWsWa3/55ZdeH5XAqpIty4qaNWvGCghu2rQp9u95eXneY0IToGrXrh1rn3TSSV4ftZFBSDKxes1Vgpd6n93nVMcKLSroHksVXHMLHJn5xRXV8dXroD4Dmzdv9mLufKCSEZs3bx5rq3MvDU488cSgfur9d+ekkOKUZvq1dz/76nHqHNR75sZUoqMab2redcevej7196hx775eoeeAsCRutZHB8uXLvZiad9X8GSLVhPa7777bi6nPj5tMPnz48JSezyzsGq/OQY1lVVC2NKpUqVLwBhr78vXXX3sxd2yFXiPr16/vxdyive+8847Xp2/fvoWe597OwzVgwAAv1qdPHy8WUqhPza+pWrZsmRdTm7f06NGjyJ7TxS8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSuR5HKV+KqSddxkt58mEe+LSigMqUyrziEkOTHVCr3qWCrBPTThs0WLFrG2Ond1fNWvrHKTpFTSVCh3k4LQRFWV5OxWRg99zdWYcZPpQpNlQxLa1SYPjRs39mIhmyekmlys+qn30a04XForl7/77rtB/dTGEm5MJfo3aNAg6FjuexY6P6j3LNVE9ZDxHDrfqWrB7rFCxtbeYgebkITZ7777zoupCs3qWu1uIlG9evX9OLt9++GHH7zYxIkTvZjaDGT8+PFFdh7ua5jqhjVmZosWLSqSc0rahAkTYvOze94XXHCB9xj12VUbEriysrK8WK1atbyYSsZ2ryGDBg3y+oQml7vOO+88L/btt996Mbd6eklYv369F0v1s0jlcgAAAAClFgsPAAAAAIlj4QEAAAAgcSw8AAAAACSuRJLLVSJVSGK3SnJVQpJoQ5O+QqqSqz7q+CoWklipEuNV5ejDDjss1g5NHg1NCDrYuAlqodVDVbIbDl6jRo0K6qc+524St/rcP/30017s4osv9mLufJCRkeH1UfODSlR3+4XMuXvjHkslAKuYSpJ0q8QvXLjQ61OzZs2g83KpCt0qsb+4RVEUm8NTrfQdUrk8yWrGB+LKK6/0YnPmzPFiI0eOTPQ8Ut14Rn3uZs+eXSTnlLTc3NzYtfHXv/517N/vuusu7zFq7lEbBLj9VIV0tbmBOpb7GqsNMW655RYv9r//+79e7NZbb421x44d6/U57bTTvFidOnW8WHFTSfxqU5kQ7vwQPOen9GwAAAAAsB9YeAAAAABIHAsPAAAAAIkrkRwPRd0b5t6D5xYM2xt1v7F7f5/KfwgpbqWOpYTcL2uW+j2h6v7mI488MtZW565i5HgAyXGLR5rpe2rdAmtmYXNN//79vdgNN9zgxV555ZVYW+WLrFmzxos1atTIi6m/yaWK8qk50L1vWxXgVMfq1q2bF3OLgn3yySdB5xBSQHDEiBFeTOUWFLe0tLSU8zrc4xRGXSvOOussL6busb/tttti7V/+8pf7cXZx9913X6yt8qhuvPFGL9ahQ4eUnzNJ6rvH2rVrS+BM9t/FF18cKxj5r3/9K/bvquik+tvUXNewYcNYW80N69at82J169b1Ym6emBrLf/nLX4Ji9erVi7VV/ue9997rxRT3O1lo/nGq1OuVat6be66h584vHgAAAAASx8IDAAAAQOJYeAAAAABIHAsPAAAAAIkr1cnlbvJP8+bNg47lFt0y85OBVHJnSIKhmV/oKzSJW3H/RpW0qYpnqSSrkAKL6m/cuXNnoY8DkBo1t6nE7lQT/JQ//elPQbEQav5xzz90Mw0VcwsU/jRRNQnqXNVmI1WrVo2133nnHa9PaUguHz9+vKWnpxe03ddTXetq167txX56jD3ca6n7muwtNm/ePC/28MMPx9qqwFr9+vW92AcffODFHn/88Vj7pJNO8vqkOt6LUmjSv/q+oL7HlAUtWrSItT///HOvT7NmzbzY9u3bvZhbtFO9TqoYofoeFfJeqOK/Ie+DmwRvFr6RQVFsDLGH+3erpHe1MVFIIVR1HVCf/RD84gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4EkkuV8l9IdWzVZKcEpKg7VbLNTNbvXq1F3MTyc1SrzauuMlSKrFy06ZNXiwvL8+LuYk+6nVQieQqqQtA0Xjuuee82JtvvunF1Oe8uKvaKqEJxaWRm+hqZrZy5UovphL73WtGz549i+q0itSiRYtiSaS5ubmxf1+xYoX3GLW5gbomusm2anOSpk2berFf/epXXqxjx46x9pgxY7w+EydO9GIzZszwYr169Yq13cR1Mz/J3kxfE0tDErdKAj7jjDNK4EwO3O233x5rv/rqq16fxYsXezH1Pcr9zqe+H6n3TyVsu9991IYS6hxUQrv7+XnllVe8Poo6VlHO6SHfRVWSeEhyeeiGSSH4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxJZJcvmvXLi+mEsFSTeK+4IILvNiGDRtibbeS+d7OK6SauXpcaAK9m1ikktmzsrK8WJcuXQo9L5UsqP4edf4AioZKXF64cKEX69Gjhxdz561f/vKXRXZeikogDImFVt8N6aeSLVUspFp6nz59vD7PPvusF8vPz/diZ599dqx96623+idbClx88cVFUu1dba6yZMmSWHvNmjWF9jHT74075lUiuTvezczOOussL+Z+DlSCu1IaEskVlVz+yCOPxNp33XVXcZ3OAXErdquxMGrUKC82ePBgLzZlypRYW42PknD88cfH2ieffHIJnUlcSKK6+txlZ2cX+riirLDOLx4AAAAAEsfCAwAAAEDiWHgAAAAASFyJ5Hhs2bLFi4XcR7xu3bqg47sFbA5W6p489TqHvq4AikazZs28mCrk6RaqUvfTK6oYYXp6eqGPC82vKA1UbpqbI3f00UcX2sdM53hcd911qZ9cGVSnTp2gGIqeKnRZnsefyr1SMdecOXO82FdffeXFpk+f7sV++OGHWFvlK6nvTI0bN/Zi//jHP/Z5nmZhOb1FLSSH6ZZbbvFibdu2LfRxKg87VaXzigIAAACgXGHhAQAAACBxLDwAAAAAJI6FBwAAAIDElUhyee3atb1YmzZtvJhbFKhbt25Bxw8pNFiUxVBKK1VsbMGCBV7smGOOKY7TAfD/U3PUX/7yFy/mzpWNGjUKOn5pLZRWlELmcFUoVhVrU69XaU2qx8Hh/vvvL+lTKHXU90QVGzBgQHGczj6VxHfMkOc87bTTUjp2SDHtUMysAAAAABLHwgMAAABA4lh4AAAAAEhcyjkee+5R3rBhQ5GcyLZt27yYW1Br8+bNXh/1/OR4/Ei9pjt27PBioa9rYfY8JuT1P1BFPf5Q9hXn+Pvp86QyBtU5qsKq7mdTFbpTz79z504vpgrnlWWqgKB7H7Ka29RrrwqrukUYC3ufy9L4Q/nENRglKXT8pUUpjtAlS5Z4yd+AmdnixYutSZMmiT4H4w97Uxzjz4wxCI3xh5LGNRglqbDxl/LCY/fu3bZ06VKrUaPGQfHrAQoXRZFt3LjRsrOzE98RhvEHV3GOPzPGIOIYfyhpXINRkkLHX8oLDwAAAAAIRXI5AAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDz2oUWLFvbYY48VtNPS0uytt94qsfMB9ldubq6lpaXZ1KlTS/pUUMYw/6G8uOyyy6xfv37B/Zk3UdQYg/9PuV54XHbZZZaWlmZpaWlWuXJla926td133322c+fOkj41HARWrlxp11xzjTVr1syqVKliDRs2tDPOOMMmTJhQ0qeGgwDzH0ob5kSUNMZgyatY0ieQtD59+tgLL7xg27Zts/fee89+85vfWKVKlez2228v6VNLyfbt261y5colfRoIcP7559v27dvt3//+t7Vs2dKWL19uH330ka1evbqkT+2A7NixwypVqlTSp4EAzH8oTcrrnIiygzFY8sr1Lx5mVrCibd68uV1zzTV22mmn2YgRI+ykk06yG2+8Mda3X79+dtlllwUfe8aMGXbKKadYtWrVrE6dOnbVVVdZfn6+mZl98MEHVrVqVVu3bl3sMYMGDbJTTjmloP3ZZ5/Z8ccfb9WqVbOmTZvaDTfcYJs2bSr49xYtWtj9999vl156qWVmZtpVV121368Bit+6dets/Pjx9uc//9lOPvlka968uXXt2tVuv/12O/fcc83sx1tXnn32Wevfv79Vr17dDjvsMBsxYkTsODNnzrQzzzzTMjIyrEGDBnbJJZfYqlWrCv591KhR1qtXL6tZs6bVqVPHzjnnHJs/f/5ez2vXrl12+eWX2+GHH26LFi0yM7O3337bOnfubFWrVrWWLVvavffeG/uv4mlpafb000/bueeea+np6fbggw8W5UuFBDH/obQImRMfeeQR69Chg6Wnp1vTpk3t2muvLRhTZmZDhgyxmjVr2ujRo61du3aWkZFhffr0sby8vII+u3btsptuuqlgTrzlllssiqLYuezvvInygTFYOpT7hYerWrVqtn379gM+zqZNm+yMM86wWrVq2ZQpU+z111+3MWPG2HXXXWdmZqeeeqrVrFnThg0bVvCYXbt22dChQ+3iiy82M7P58+dbnz597Pzzz7fp06fb0KFD7bPPPis4xh5//etf7aijjrJvvvnG7rrrrgM+dyQvIyPDMjIy7K233rJt27bttd+9995rP//5z2369Ol21lln2cUXX2xr1qwxsx8nyVNOOcU6depkX375pY0aNcqWL19uP//5zwsev2nTJrvpppvsyy+/tI8++sgqVKhg/fv3t927d3vPtW3bNrvwwgtt6tSpNn78eGvWrJmNHz/eLr30Uhs0aJB999139s9//tOGDBniLS7uuece69+/v82YMcMuv/zyInqVUNyY/1BSQubEChUq2BNPPGHffvut/fvf/7aPP/7YbrnlllifzZs321//+ld76aWX7NNPP7VFixbZ73//+4J/f/jhh23IkCH2/PPP22effWZr1qyx4cOHx46xP/Mmyg/GYCkRlWMDBw6MzjvvvCiKomj37t3Rhx9+GFWpUiX6/e9/H5144onRoEGDYv3PO++8aODAgQXt5s2bR48++mhB28yi4cOHR1EURf/617+iWrVqRfn5+QX//u6770YVKlSIli1bFkVRFA0aNCg65ZRTCv599OjRUZUqVaK1a9dGURRFV1xxRXTVVVfFzmH8+PFRhQoVoi1bthScQ79+/Q7gVUBJeeONN6JatWpFVatWjXr06BHdfvvt0bRp0wr+3cyiO++8s6Cdn58fmVn0/vvvR1EURffff390+umnx465ePHiyMyinJwc+ZwrV66MzCyaMWNGFEVRtGDBgsjMovHjx0ennnpq1KtXr2jdunUF/U899dToD3/4Q+wYL730UtSoUaPYed54440pvgooKcx/KG0KmxNdr7/+elSnTp2C9gsvvBCZWTRv3ryC2JNPPhk1aNCgoN2oUaPooYceKmjv2LEjatKkScFnQdnbvPnNN9+k8FeiNGMMlrxy/4vHyJEjLSMjw6pWrWpnnnmmXXTRRXbPPfcc8HFnzZplRx11lKWnpxfEevbsabt377acnBwzM7v44ott3LhxtnTpUjMze/nll+3ss8+2mjVrmpnZtGnTbMiQIQWr8IyMDDvjjDNs9+7dtmDBgoLjdunS5YDPF8Xv/PPPt6VLl9qIESOsT58+Nm7cOOvcubMNGTKkoE/Hjh0L/n96erplZmbaihUrzOzH8TF27NjY+Dj88MPNzAp+kp07d64NGDDAWrZsaZmZmdaiRQszs4LbqPYYMGCAbdq0yT744APLysoqiE+bNs3uu+++2HNceeWVlpeXZ5s3by7oxxgsm5j/UJoUNieOGTPGTj31VGvcuLHVqFHDLrnkElu9enVsLqpevbq1atWqoN2oUaOCOXP9+vWWl5dn3bp1K/j3ihUremModN5E+cMYLHnlfuFx8skn29SpU23u3Lm2ZcsW+/e//23p6elWoUIF7567HTt2FOlzH3vssdaqVSt77bXXbMuWLTZ8+PCC2wzMzPLz8+3Xv/61TZ06teB/06ZNs7lz58YG9U8v7ihbqlatar1797a77rrLJk6caJdddpndfffdBf/uJmmnpaUV/NSan59vffv2jY2PPWP5hBNOMDOzvn372po1a+yZZ56xyZMn2+TJk83MvNtpzjrrLJs+fbpNmjQpFs/Pz7d77703dvwZM2bY3LlzrWrVqgX9GINlE/MfSpu9zYm5ubl2zjnnWMeOHW3YsGH21Vdf2ZNPPmlm8flMzZnuWC5M6LyJ8okxWLLK/a5W6enp1rp1ay9er149Lxlo5syZdvLJJwcdt127djZkyBDbtGlTwYVxwoQJVqFCBWvbtm1Bv4svvthefvlla9KkiVWoUMHOPvvsgn/r3Lmzfffdd/L8UD4dccQRwbUQOnfubMOGDbMWLVpYxYr+R3X16tWWk5NjzzzzjB1//PFm9mOyrnLNNddY+/bt7dxzz7V3333XTjzxxILnyMnJYQyWU8x/KO32zIlfffWV7d692x5++GGrUOHH/yb63//+d7+OlZWVZY0aNbLJkycX/MeZnTt32ldffWWdO3c2s/2bN3FwYAwWr3L/i8fenHLKKfbuu+/au+++a7Nnz7ZrrrnG24FlXy6++GKrWrWqDRw40GbOnGljx46166+/3i655BJr0KBBrN/XX39tDz74oF1wwQVWpUqVgn+79dZbbeLEiXbdddcV/FfJt99+20uuRNmzevVqO+WUU+w///mPTZ8+3RYsWGCvv/66PfTQQ3beeecFHeM3v/mNrVmzxgYMGGBTpkyx+fPn2+jRo+1//ud/bNeuXVarVi2rU6eO/etf/7J58+bZxx9/bDfddNNej3f99dfbAw88YOecc07BJDd48GB78cUX7d5777Vvv/3WZs2aZa+99prdeeedRfI6oHRi/kNxK2xObN26te3YscP+9re/2ffff28vvfSS/eMf/9jv5xk0aJD96U9/srfeestmz55t1157bWxs7++8ifKDMVg6lPtfPPbm8ssvt2nTptmll15qFStWtN/+9rfB/7XP7Md7/EaPHm2DBg2yY4891qpXr27nn3++PfLII7F+rVu3tq5du9oXX3wRqwJs9uP9/Z988on93//9nx1//PEWRZG1atXKLrrooqL4E1GCMjIyrFu3bvboo4/a/PnzbceOHda0aVO78sor7Y477gg6RnZ2tk2YMMFuvfVWO/30023btm3WvHlz69Onj1WoUMHS0tLstddesxtuuMHat29vbdu2tSeeeMJOOumkvR7zxhtvtN27d9tZZ51lo0aNsjPOOMNGjhxp9913n/35z3+2SpUq2eGHH27/+7//W0SvBEoj5j8Ut8LmxGrVqtkjjzxif/7zn+3222+3E044wf74xz/apZdeul/P87vf/c7y8vJs4MCBVqFCBbv88sutf//+tn79ejP7cdei/Z03UT4wBkuHtGh/b0wDAAAAgP100N5qBQAAAKD4sPAAAAAAkDgWHgAAAAASl3Jy+e7du23p0qVWo0YNS0tLK8pzQhkVRZFt3LjRsrOzC7aiSwrjD67iHH9mjEHEMf5Q0rgGoySFjr+UFx5Lly61pk2bpvpwlGOLFy+2Jk2aJPocjD/sTXGMPzPGIDTGH0oa12CUpMLGX8oLjxo1ahQ8QWZmZqqHOWCbNm3yYg888IAX21MVco8BAwZ4fa688sqiO7EUDR8+3Iu9+OKLXqx3795e7Nprr03knEJt2LDBmjZtWjA2klRaxl9pMHfuXC82ZswYL1arVi0v9tO6CmZm3bp18/pkZ2cfwNkVTm2sl8p/QSvO8WfGGEQc4w8ljWswSlLo+Et54bHni0FmZmaJDrpDDjnEi7lfpszMq/xcrVo1r09p+PBUr17di6mq1VWrVvVipeH8zVL70pjqc5T0+CsNMjIyvJgaH2rMu/3UhJH061tUC4+ieGwqz8MYxE8x/lDSuAajJBU2/kguBwAAAJA4Fh4AAAAAEpfyrVYl4eqrr/Zin3zyiRfbvXu3F2vQoEGsfdddd3l9nnjiCS+mkqcOO+ywWDsrK8vrs2bNGi82ceJEL7Z9+/ZYe8OGDV6fRo0aebGnn37ai73zzjux9jPPPOP1admypRdD6ZDqLUfXXHONF/viiy+82M6dO73Ytm3bCj3+//7v/3qxadOmebHNmzfH2ieccILX5+GHH/Zi6hawXbt2xdrqlkoAAFC28IsHAAAAgMSx8AAAAACQOBYeAAAAABJXqnM8Pv7441h7wYIFXp9OnTp5MZUn4eZ9HHXUUV6flStXerH58+d7Mbd2SJcuXbw+06dP92JqW9y6devG2urvWbFihRc79NBDvdi6deti7d/97ndeH1UnBKVDqjkey5Yt82KqZoebT2RmVrly5VjbHUNmZv/5z3+82NatW71YpUqVYu1vv/3W66M+Ayq3yj1XlQcCAADKFn7xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFypTi7/8MMPY+0WLVp4fVQBNDfJ1cxsx44dsbab1G2mE19Vwq9b3Ewl0apk2IyMDC9Wo0aNWPuHH37w+lSvXj3ovJo0aRJrqyT7zz77zIv16tXLi6H4qcKXFSr4/23ATbxetGiR1yc9Pd2LqQKC7kYJaoyqRHW10YObqK7G6G9/+1svpqi/GwAAlG1c3QEAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABJXqpPLly5dGmtnZmZ6fUKTy92EcPU4NznWTCfbqgrQrkMOOcSLqWTvzZs3x9oqkVydg0q+df9GVfWa5PLSQSVeq+Ry5eOPP4613QRxM3/TgtDjq7Gtjq8+P+4GDh07dgw6lqq83rBhw1g7NPEeAACUXly5AQAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEldqkstV8qibjJ2VleX1UbGtW7cW+nxuIqyZn5xtZpafn+/F3ArQKildHV/9je6xVB91rKpVq3oxl0ounzNnTqGPQ/LUe6PGkTJlypRY203ENjOrWbOmF8vJySn0PNTmBitXrgw6L3fzh/POO8/r88EHH3ixY445xou5f5NKxgcAAGULv3gAAAAASBwLDwAAAACJY+EBAAAAIHGlJsdjwYIFXszNd9iyZYvXRxUVrFWrlhdz8yQ2btzo9alY0X85VEE1935zlVOi7klXhQ3dHA/1OJUPoIqnqfvzXT/88EOhfZC80PdZGTt2bKF9VI5H7969vdj3339f6DmoHI+jjz7ai02dOjXWVp+d888/34s1b97ci7lUQU6Ubrm5uV5syZIlXowCpgBw8OAXDwAAAACJY+EBAAAAIHEsPAAAAAAkjoUHAAAAgMSVmuTyvLw8L1alSpVYWyVUqyRdlazqFgesUaNG0LFUAUE3SVydl0okV4UAq1WrFmurJFpVWK5Ro0ZebNOmTbG2Ovc6dep4MZU8XK9ePS+GoqOKVarNDRQ3IXzz5s1en88//9yL1a5d24u5Y14V5DzppJO8mEoSHjBgQKz9hz/8weujHEiiPUqH119/3YvdddddXqxPnz5ezN0IoX379kV2XgfiP//5T6zdpk0br0/Xrl2L63QAoFzgFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrk8tWrV3sxN4F6/fr1Xp9PP/3Ui1188cVeLDs7O9ZWyezbtm3zYm7yt5lO9napRGH1OLdyuXpc/fr1vZhKHnaT3Nu1a+f12bBhgxebPXu2FyO5PFmhlbjHjx/vxVasWBFrq2Rc9Xlau3atF6tVq1asrTYaaNiwoRebN2+eF1PjDaXX7t27vZjaKOOHH37wYjfccEOhfVq2bOnFpk+f7sWuuuqqWHvixIn+yQZyN9R4/vnnvT6rVq3yYlu2bPFiGRkZsbZ7DcH+cTeROJANJJ544olYu3Pnzl6f0Oume63r2LGj16dx48b7e4oH7I9//GOsfeSRR3p9zj333OI6HaDI8IsHAAAAgMSx8AAAAACQOBYeAAAAABLHwgMAAABA4kpNcrlKat24cWOsPXbs2KDHffXVV17shBNOiLVVkqNbQddMJ4S7SZmqSvn27du9mJtIbma2devWWNutPm6mK7FXr17di02ePHmfxzYza9KkiRebNm2aFzv++OO9GIpOaGKlWz3ZzE/SVONKVahXGyW4Y1cdSz1OufDCC2Ptm266yevzyCOPeDH1WhRlIio0VTFeWbNmjRfLycmJtVu0aOH1CU3udedwNeZPPvlkLzZy5EgvNnz48FhbJY2ruW3gwIFerLRUUC8vdu3aFWurjVSUMWPGeLFf/OIXsbbaDMUdC2ZmU6dO9WLutfSpp57y+qiNEo499lgvdswxx8TaasON3NxcL/bRRx95sYULF8baaiyTXF56qflVjWV3bLVq1SroWGX5msgvHgAAAAASx8IDAAAAQOJYeAAAAABIXKnJ8fjf//1fL9a7d+9Ye926dV4ft5CQmS4a5RbJq1q1qtdH5XOoXA23yNaOHTu8PuqePHV89/5SN6/FzOyLL77wYq+//roXc++fV0W9/vGPf3ixKlWqeDEULff+5tACgh988IEXc/M31Pu8efNmL6bGaUgxTFWMULnkkktibfU3nnfeeV7s7bff9mJl+f7VJKmif+q1Cnn9Qsdghw4dvFjt2rVj7W+//dbr4xanNPPvgTfzx9f111/v9VG5aUcddZQX+93vfhdrqzwNtzDt3oTkUqn8voNNaCFKN6dj1qxZXh91XVuyZIkXe++992JtNdbUe9OsWbNCzysrK8vro2KLFy/2YlOmTIm1Ve6Jym35+c9/7sXcIsdz5szx+kBLOifi+++/j7Xvu+8+r4/Ke/vkk0+8WN++fWNtlRtZEtfDv//977H20Ucf7fXp1atXSsfmFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDElZrkcsUtnPfmm28GPU4lFI4fPz7WVsmKoQW1XCq5TsXcBGMzs8zMzFhbJfKqx7nJnWZmDzzwwD7PEyUnJDlMFbVUxaYOPfTQWHvbtm1eH7V5QtOmTb2YmyTXuHFjr49KFFXcz+uECRO8PhdffHHQsQ5GIUm6oe9F0v7yl7/E2qeeeqrXR20akJGR4cXchN8GDRp4fdxERzOzE088sdDzPBDuZ7a8J5Kr658bU31CNykYNWpUrP3oo496fa677jovpor3hSRaL1++3IupedjdiCM9Pd3roz6bqrCq20+Nd7fQqpn+XLvJ62vXrvX6uIn3anOasizkO1mqm2uozVbUJhkjRozwYm7ivzJjxgwvpgo+uu+r+13VrGgLOqsC29dee60Xc8+/X79+Xh+SywEAAACUWiw8AAAAACSOhQcAAACAxLHwAAAAAJC4UpNcrpKI3EQtleClEv5UpV03yUslH6njq2q1buXR0IRPdSz3PNxK5ma6QmoIlZSuhCYHInUhY0RVKVfj2600r5Lk1FjLz8/3Ym5ienZ2ttdn5cqVQee1aNGiWPuuu+7y+iiXXXaZFxsyZEjQY0uDKIpi81dIYqOa70LGyLJly7zYSy+95MXef/99L/bxxx8XevxQ3bp1i7VV5WV1DqpqszvvqqRdVdE6JLlczYHr16/3YuqzsWXLllh76dKlXp+fVsxWxyhLQsakukbm5OR4sbZt23qxe++9N9Z+/vnnvT6bNm3yYu5mGmZmv/rVr7xYqtatWxdrjx492uszdepUL+ZuzGHmJ6a3atXK66PmU5UI7ya9qznXTS5Xr1/S3PnPHUepJn/vT78Q7vXpjjvu8Pqo8a2q3btVydVmPzVq1PBiKlG9Zs2asfbw4cO9PpMnT/ZiderU8WLuGJk9e7bXx30dzMx69uzpxdyNbWbOnOn1SRW/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSu1CSXqyQiN+k5NIlbVQt1Va5c2Ytt3brVi6lkSDdhMTRRXZ2/+5yqaqo61xDq+YoyWQuaSmh1x7KqNv7EE094saOPPtqLucmc27dv9/qoMaOS3Vx169b1YvPnz/di6m/MzMyMtVWCuFvd3Mxs3LhxXmzkyJGx9jnnnOP1Ka3cz/6BfOZuvPHGWPuLL77w+rivu5mucuxWp33qqadSPi/XP//5Ty/26quvejH1XrtJjKr68r///W8vpjbd6N27d6ztJt+amW3YsMGLhWwiopJ7DzvssIL/7yajlxYqaVyNSXW9cMebGleqqvwpp5zixd59991Y233fzXTSuNpYwBXy/u2Nm9x70UUXeX1UTCXbPvnkk7H2hx9+6PVRm4GojQvcef2nGxmUJmlpabHxlOp8p74zuRs2rFq1yuujkqXXrFnjxebOnRtrN23a1Otz1FFHeTG1sYB7/VNzqXq/TjvtNC/mUtduNY+p+c8dM+5GNGZm9erV82LuRgZmZmeddVasrTZAcDczCN3cgF88AAAAACSOhQcAAACAxLHwAAAAAJC4UpPjEULdV67u41T3UIbcr6sKQKliVm5ehjqWul9Rnat7X7C6v69NmzZeLETovb0oWiEFGR944AEvpu7ZdO8/NvPvTVUFvFTeh8ofCqH+npAcJvXZUbktVatW9WLvvfderK3uy//lL3/pn2wJKKp7nJUjjzwy1n755Ze9Pj/NM9ijdevWXswtTHXbbbd5fVSxrBBqDlT3S6t7od0xoYpZderUyYupQrFuYa+uXbsW+nx7487Xq1ev9vrUr1+/4P+XVAHB3bt3xz6P7vgLHY9PP/20F3PzMNzxaGZ20kkneTGV2+D2++yzz7w+7n3lZmHXP/U3hl7/QgreKSonz83VUN9ZVA6Tmt/cuV/lrroFX9Wxi5t7bQgtYqfyMtxinyoXQeUuqpwf930+4ogjvD6ffvqpF1OF+ho0aBBr/3Qe2EO9p02aNPFiLpUnoeZSt/ClmX+NV3OSeg1Vkc6srKxYW+UXujk3oeOPXzwAAAAAJI6FBwAAAIDEsfAAAAAAkDgWHgAAAAASV6aSy0P98MMPXsxNflTFAhWV6KMSKV2qGFNI0nto4UFVUMZNXFLJdShaoe+XSxXXU4nkKuHcLRinkmznzZvnxVTxLzcZVyWjhYx3RRVUU4mAqthYURa3S9r27dtjyfxu8p6bpGcWnsB65ZVXxtqqKJ9K7h08eLAXO+6442Lt0aNHF/p8ZnoMfv7557H2999/7/VRc2zHjh292LHHHhtrq00QVEK4Kkb55Zdfxtrq3FVSpiom5n621Zz702ToVAu9HqgKFSoEF9fdF5X46ib1q6RdtblB+/btvZj7+nXu3LnQPma6CJorZEOPvQn5LKrPyjPPPOPF+vTpE2vPmTPH66OKtFavXt2LufOG+hvd5HKV0Jy0oUOHxjYScTetuPzyy73HqCJ2qhCqm9itXjuVdL9y5cpCn1Mls6uCvWp8u9e26667zuujvn+p66s7t6kNCdS1W1mxYkWsrQouhm6s9PXXX8faquBnqvjFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHFlKrk8NCFz0qRJXsxNqFGVnVWisEpscxOLVB+VCKaSbd1ESpW8px7nJhGZ+cmB6u85kCS8g01IRdvQpM533nkn1lZJlCq5XL33bgKhqhaqKp2qMb9w4cJYWyW2qfNSf7e7UYLSsmVLL/bcc88V+rjSbMGCBbEERzcRVSUZqs+mqvTuJk6qhG23Irl6nJmf/HzVVVd5fVTCpdoUwz3W4Ycf7vVRCeFu8q2Z2ZQpU2Ltxo0be30Ut6qxmdnxxx8fa0+fPt3rc+qpp3ox9Xl05+K2bdt6fX76OSiKBO+SpCoThyS1Ll++3ItVrVrVi7mbAbhVvs3M5s+fX+jzKeq6mZeX58XUmHE3o1EbyqhzHTZsmBdr1qxZrF2rVi2vj9o8QSX8up8xVXHanXND5uCi1rt379imIe45qHE1c+bMlJ5LbU6irpELFizwYu55qflJHUvF3OukGn9qrKljufOGGgtqDlbJ+O7YUteB0O/R7vda9Tn/6quvYm01RpWyPVMCAAAAKBNYeAAAAABIHAsPAAAAAIlj4QEAAAAgcWUquTw0eU9VbXaTqlWSj0q+VYnjbqKPSugKTeJ2k0VVBVOVWJSTk+PF3EqwoUlE0Iry9XMrSbsVw810hVRVedkdM6qi6GeffebF2rRp48Xcz9TYsWO9Pmp8q0RoNU5dKjk6hErQLi3jOz09PZbE5yZaq9d99uzZXkwlC7oJfaoKsBo3Krly0KBBsXa/fv28PqqadMi8OHfuXK+Pqiw+Y8YML+ZuXqCSPtU5qPHmnofaGGH8+PFeTG3G4G4AoBKM69evX/D/1WeiOEyaNCmWVPrmm2/G/r1Ro0beY9Troq49bqK1+vyqv1tVQp41a1asrT7TqoL8qFGjvJibzKvmKJUkHrIphkr+VpsnqGO58/V3333n9VHjVsXcJGO1ocwVV1wRa6uNRpJWsWLF2Ln+4he/iP272y4O6vV03y81z6jEbjVOQ65j6jugOr4bKy3XtRDueHM3vtkbfvEAAAAAkDgWHgAAAAASx8IDAAAAQOJKdY6He4+tuqdSFW5ZuXKlF3PvSVb30al7+RT3fmOVB6LuOVXHd+8DVIXF1H2BKsfDVdYLWpU26r1Rr7FbEM3MbOrUqbF2vXr1gh6n7sU+9NBDY+3WrVt7fdS9vl9//bUXcwsM9erVy+vz+eefezF1z71b8Ep9xrKysrxYiNJ832u1atVi97m7BclUUT51L2zt2rW9mFvATY0blQd09NFHe7FFixbF2iqfQ+VgqCJobiGv7Oxsr4/KiVD3RrvF5tS91yqmPo/u66MKYqoxuGzZMi/mXlvU/P3TvAh1LSoO7dq1i+UHuePPbZuZrV692os1aNDAi7n5Ier9U2N51apVXsx9D1UeiHqNH3jgAS/m5sipwmWh74f7nOoc1OdCjSM3puatkFw4M7Mjjjgi1lbv46WXXprSsYtSZmZmbPy540GND/W5VDkR7veo0Mcp7nuh5hRV+FIdX809LjWOQr5jhhacVTH3b1KfgdDXyz2+uub/NMfNLDyHk2+mAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSvVyeUhiTgqcalOnTpezC2+o4p1qYRclditEpxcKqlH/T3usVQymjqWKpLoUonPpbkQW3EJTdRyX7/QZP1bb73Vi7lJa+o1V4ltKqHQLRioHte2bVsv5iYrmvkF6hYuXOj1ad++vRdTBfDcpDU32dxMJyqXdTVq1IjNJ+44UXONGm9qkwo3iVvNbSpJVxUuc59TFSRTxQjV/OMmMaq/R22MoAqxucn3quCdGm/q9XLPSyUAqyR+lRTZrFmzQs/hp5szhCScJqFmzZqxMXbRRReldBx1XXNfF1WUT40/9Vq412o1P6jkaDVXrlu3rtDnUxseqLnSHd8qUd19PvU4M//7gnptVHKvmiPcgo5NmjTx+rhjObSAW5Lcv0X9bSif1OdL4RcPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxJX55HJVtVclWIVUrVSVGUMqj4ZU493bsbZs2RJru8mkZroKekhinkqMV0l4oZUsywJ3zKhkRfXapVrl/S9/+YsXU5W+TzzxxFh74sSJXh/1PqjkWDepUf2NeXl5XkwlE7ueffZZL6b+HrcSu5mfRKjOS1XeLusqV64cG1Pue5aTkyMf43KrlJuZrV+/PtZ2K82bhVe6dan3R1VZD6lCrTbmUOegnjOk2rJKUFWfWXfcq+uDm7RrphPV3flaVYP/6TmkOoeUFmr+SU9P32fbzK9eDAD7UrZnSgAAAABlAgsPAAAAAIlj4QEAAAAgcSw8AAAAACSuVCeXh1DVZFVyuVvhUyVyhlZSdZN7Q5PL1fHdiqgqIVwdSz2nm4hat25dr09Iwn5Z5iaEqtdcUVV7Fy1aFGv/7W9/8/o8+uijXqx79+5ebNmyZbF2jx49vD5ff/21F1NJu24irNq0IDTRdcSIEbF23759vT7vvfde0LHc51RjTSXLK+5j1d9YWv3sZz+LtVWS9dy5c72YO0bM/A0Bvv/+e6+PSvhV84O7SUXIxgVmZoceeqgXcyvQq00xVLKyqkruHutAkrTdz7HaUEHNsWqzEff8Q8cuAGDv+MUDAAAAQOJYeAAAAABIHAsPAAAAAIkr8zkequCVul/Xvb/ZzYcwM6tTp44XU/f+u/ebq3u41b3SqviXm+Oh7pVWx1fn5d4jrnI8DjZvvPGGF/uf//kfL6beL3WfvEvd9/3tt996sWOOOSbWnj59utenVatWXmzmzJlezD1Xdc+6yhcYPny4F1M5HS411kKovIzs7Oygx7pjviwXuVQ5C23btg2KYf+440TllAAASg6/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSuVCeXhxS7W7BggRdTybau/Px8L9ayZUsvphLVXSpR3S2KZaaL2bnnsWXLFq+PWzDOTCecq2JzrvJeQDAvLy/Wvvnmm70+7kYDZjrxP4RKvFZjZtKkSbH2cccd5/VRxeHUeblF0TZt2uT16d+/vxfr16+fFwsRWoTRTexVSdU1a9YMOlZ5H6cAAByM+MUDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcaU6uTyEqmhctWpVL+YmbauEbZWUvn37di/mJvOq6umHHnpo0LFcKllZ/Y07duzwYqpStEslpZcnI0aMiLXVe9OwYUMvphK03fdCVTJXr6dKqnaTpadMmeL1adKkiRfr0qWLF/v6669j7dzcXK/Pm2++6cUUNxFefS7S09ODjhUyvhs0aBB0LAAAUP7wiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiynxyuaqqrJKx3cTX+vXre31UUrBKtnWPpZ6vdu3aXmzz5s1ezE3cVRWbQ5LGzXRSvUv9jeXJpZdeGmv/97//9frMmjXLi6lK9u7rrhLJ1XuvXuNq1aoVeqz58+d7MbdKuZnZunXrYu2xY8d6fUKpKu4utelCyLF27tzp9QmtEO8m9oecJwAAKN3K97dQAAAAAKUCCw8AAAAAiWPhAQAAACBxZf7G6Tlz5ngx9x54M/8+9bVr13p9VEwVRVu9enWsvWHDBq/PvHnzvNjy5cu92NSpU2Pt7t27e31U/oHKBVH5LgcbN5fio48+8vosWbLEiw0ZMsSLvfvuu7G2W7jPLKxo3oFQRQvfe++9WPukk05K9BwOO+ywoH7u565ly5ZenyOPPDLoWCp3BgAAlG384gEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJC4Up1cHlLsrkuXLl5s1apVXswtGKgKA9arV8+LqSTXpUuX7rNtZnbMMcd4sW3btnmxhQsXxtqqWGD16tW9mJuUbmbWsGFDL+Yq7wUEQzRp0sSL3XnnnUExl9rc4Pvvv/di7sYFqsCkSsYOTexO0s033+zFjj32WC/mfsbU31inTp2g56RgIAAA5Q/fQgEAAAAkjoUHAAAAgMSx8AAAAACQuJRvpN5TwE4Vzysqu3btirVVvoUqsKZyKdx+u3fv9vps3rzZi6nn3LJlS6HPp44Vcl4qx0PlZajCde57oe6Td19Ts6Ir1rbn+VVxw6JWHOMvhCruuGnTJi/mjgeVY6SOlfTfF/IZU9Tf6J6/W7TTTOcrFZXiHH8/fZ6SHoMoHRh/KGkH4zUYpUfo+Et54bFx40YzM2vatGmqh0ARe/nll0v6FMzsx7GRlZWV+HOYMf7gK47xt+d5zBiDiGP8oaRxDUZJKmz8pUUpLo13795tS5cutRo1asj/So+DTxRFtnHjRsvOzk589yzGH1zFOf7MGIOIY/yhpHENRkkKHX8pLzwAAAAAIBTJ5QAAAAASx8IDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACSOhQcAAACAxLHwAAAAAJA4Fh7/v3vuuceOPvroffY56aST7MYbbyyW80H5V9iYGzJkiNWsWfOAnuOyyy6zfv36HdAxgAO1v+MwNzfX0tLSbOrUqYmdEw4ejD+UFMaSr8wuPNLS0vb5v3vuuafIn/PNN9+0+++/f599Chtk9957r/3qV78ysx//hrfeequIzxLFZdKkSXbIIYfY2WefXdKnUuJYlJcNK1eutGuuucaaNWtmVapUsYYNG9oZZ5xhEyZMKOlTw0GA8YfixHgrnSqW9AmkKi8vr+D/Dx061AYPHmw5OTkFsYyMjCJ/ztq1a+/z37dv317oMd5++2277bbbiuqUUIKee+45u/766+25556zpUuXWnZ2dkmfErBP559/vm3fvt3+/e9/W8uWLW358uX20Ucf2erVq0v61HAQYPyhOJXX8bZjxw6rVKlSSZ9GysrsLx4NGzYs+F9WVpalpaXFYmrhMW7cOOvataulp6dbzZo1rWfPnrZw4cJYn5deeslatGhhWVlZ9otf/MI2btxY8G/uf9Vt0aKF3X///XbppZdaZmamXXXVVXbooYeamVmnTp0sLS3NTjrppIL+ixcvtm+//db69OljLVq0MDOz/v37W1paWkHbzOzpp5+2Vq1aWeXKla1t27b20ksvxc4xLS3Nnn76aTvzzDOtWrVq1rJlS3vjjTdSfCWRivz8fBs6dKhdc801dvbZZ9uQIUNi/z5u3DhLS0uzjz76yLp06WLVq1e3Hj16xBbHrvnz51vLli3tuuuusyiKZJ+3337bOnfubFWrVrWWLVvavffeazt37iz0fO+9916rV6+eZWZm2tVXXx1bJG/bts1uuOEGq1+/vlWtWtV69eplU6ZMiT3+k08+sa5du1qVKlWsUaNGdttttxU872WXXWaffPKJPf744wW/OObm5hZ6Tihe69ats/Hjx9uf//xnO/nkk6158+bWtWtXu/322+3cc881M7NHHnnEOnToYOnp6da0aVO79tprLT8/v+AYe27/Gz16tLVr184yMjKsT58+sf8QtGvXLrvpppusZs2aVqdOHbvlllu88Txq1Cjr1atXQZ9zzjnH5s+fXzwvBEoE4w/FKWS8paWl2bPPPmv9+/e36tWr22GHHWYjRoyIHWfmzJl25plnWkZGhjVo0MAuueQSW7VqVcG/7+9Y2rVrl11++eV2+OGH26JFi8ys8Ov6nu985557rqWnp9uDDz5YlC9V8YvKgRdeeCHKysraZ58dO3ZEWVlZ0e9///to3rx50XfffRcNGTIkWrhwYRRFUXT33XdHGRkZ0c9+9rNoxowZ0aeffho1bNgwuuOOOwqOceKJJ0aDBg0qaDdv3jzKzMyM/vrXv0bz5s2L5s2bF33xxReRmUVjxoyJ8vLyotWrVxf0//vf/x6dfvrpURRF0YoVKyIzi1544YUoLy8vWrFiRRRFUfTmm29GlSpVip588skoJycnevjhh6NDDjkk+vjjjwuOY2ZRnTp1omeeeSbKycmJ7rzzzuiQQw6JvvvuuwN9KRHoueeei7p06RJFURS98847UatWraLdu3cX/PvYsWMjM4u6desWjRs3Lvr222+j448/PurRo0dBn7vvvjs66qijoiiKomnTpkUNGzaM/u///q/g391x/emnn0aZmZnRkCFDovnz50cffPBB1KJFi+iee+7Z63kOHDgwysjIiC666KJo5syZ0ciRI6N69erFxvUNN9wQZWdnR++991707bffRgMHDoxq1apVMHaXLFkSVa9ePbr22mujWbNmRcOHD4/q1q0b3X333VEURdG6deui7t27R1deeWWUl5cX5eXlRTt37kz5tUUyduzYEWVkZEQ33nhjtHXrVtnn0UcfjT7++ONowYIF0UcffRS1bds2uuaaawr+/YUXXogqVaoUnXbaadGUKVOir776KmrXrl30y1/+sqDPn//856hWrVrRsGHDou+++y664ooroho1akTnnXdeQZ833ngjGjZsWDR37tzom2++ifr27Rt16NAh2rVrVxRFUbRgwYLIzKJvvvkmkdcCxY/xh+IUMt7MLGrSpEn0yiuvRHPnzo1uuOGGKCMjo+Dat3bt2qhevXrR7bffHs2aNSv6+uuvo969e0cnn3xywTH2Zyxt3bo16t+/f9SpU6eC73wh13Uzi+rXrx89//zz0fz58wu+t5ZVB83CY/Xq1ZGZRePGjZP/fvfdd0fVq1ePNmzYUBC7+eabo27duhW01cKjX79+sePsa8Lq3bt39Pe//72gbWbR8OHDY3169OgRXXnllbHYhRdeGJ111lmxx1199dWxPt26dYtN0EhWjx49osceeyyKoh8nuLp160Zjx44t+Pc9C48xY8YUxN59993IzKItW7ZEUfT/Fh4TJkyIatWqFf31r3+NPYc7rk899dToD3/4Q6zPSy+9FDVq1Giv5zlw4MCodu3a0aZNmwpiTz/9dJSRkRHt2rUrys/PjypVqhS9/PLLBf++ffv2KDs7O3rooYeiKIqiO+64I2rbtm1sYfXkk08WHCOK/M8GSqc33ngjqlWrVlS1atWoR48e0e233x5NmzZtr/1ff/31qE6dOgXtF154ITKzaN68eQWxJ598MmrQoEFBu1GjRgVjJ4p+/Hw0adIk9sXPtXLlysjMohkzZkRRxBe/8orxh+JU2Hgzs+jOO+8saOfn50dmFr3//vtRFEXR/fffX/Afi/dYvHhxZGZRTk6OfM69jaXx48dHp556atSrV69o3bp1Bf1DrutmFt14440pvgqlT5m91WpfFi1aZBkZGQX/+8Mf/mC1a9e2yy67zM444wzr27evPf7447GfZ81+vHWqRo0aBe1GjRrZihUr9vlcXbp0CTqnDRs22CeffFLwE9/ezJo1y3r27BmL9ezZ02bNmhWLde/e3Wu7fZCMnJwc++KLL2zAgAFmZlaxYkW76KKL7LnnnvP6duzYseD/N2rUyMwsNqYWLVpkvXv3tsGDB9vvfve7fT7vtGnT7L777ouN7SuvvNLy8vJs8+bNe33cUUcdZdWrVy9od+/e3fLz823x4sU2f/5827FjR2zMVapUybp27VownmbNmmXdu3e3tLS0gj49e/a0/Px8W7JkyT7PGaXL+eefb0uXLrURI0ZYnz59bNy4cda5c+eCWwXHjBljp556qjVu3Nhq1Khhl1xyia1evTo2vqpXr26tWrUqaP90nly/fr3l5eVZt27dCv69YsWK3jw5d+5cGzBggLVs2dIyMzMLbjXdc+sByifGH4pTYePNLH6NTk9Pt8zMzILxNG3aNBs7dmzsmnv44YebmRXcThU6lgYMGGCbNm2yDz74wLKysgriodf10O+aZUG5XHhkZ2fb1KlTC/539dVXm5nZCy+8YJMmTbIePXrY0KFDrU2bNvb5558XPM5N1klLS7Pdu3fv87nS09ODzun999+3I444wpo2bbqffw1Km+eee8527txp2dnZVrFiRatYsaI9/fTTNmzYMFu/fn2s70/H1J4v7j8dU/Xq1bOuXbvaq6++ahs2bNjn8+bn59u9994bG9szZsywuXPnWtWqVYvwL0R5VrVqVevdu7fdddddNnHiRLvsssvs7rvvttzcXDvnnHOsY8eONmzYMPvqq6/sySefNLP4xhlqnoz2kpO0N3379rU1a9bYM888Y5MnT7bJkyd7z4PyifGH4rS38bbHvr735efnW9++fWPX3KlTp9rcuXPthBNOMLPwsXTWWWfZ9OnTbdKkSbF46HU99LtmWVAuFx4VK1a01q1bF/zvp7tRderUyW6//XabOHGitW/f3l555ZUife7KlSub2Y8JRD/19ttv23nnnReLVapUyevXrl07b6u3CRMm2BFHHBGL/XTBtKfdrl27Azp3FG7nzp324osv2sMPPxybKKZNm2bZ2dn26quv7tfxqlWrZiNHjrSqVavaGWecEdvMwNW5c2fLycmJje09/6tQYe8f5WnTptmWLVsK2p9//rllZGRY06ZNCzYx+OmY27Fjh02ZMqVgzLVr184mTZoUu7hPmDDBatSoYU2aNDGzH8e9O5ZRNhxxxBG2adMm++qrr2z37t328MMP23HHHWdt2rSxpUuX7texsrKyrFGjRgUXX7MfPzNfffVVQXv16tWWk5Njd955p5166qnWrl07W7t2bZH9PShbGH8oTnvGW4jOnTvbt99+ay1atPCuuenp6fs1lq655hr705/+ZOeee6598sknsedI5bpelpXZ7XT314IFC+xf//qXnXvuuZadnW05OTk2d+5cu/TSS4v0eerXr2/VqlWzUaNGWZMmTaxq1aqWnp5u77//vv3+97+P9W3RooV99NFH1rNnT6tSpYrVqlXLbr75Zvv5z39unTp1stNOO83eeecde/PNN23MmDGxx77++uvWpUsX69Wrl7388sv2xRdfyFt9ULRGjhxpa9eutSuuuCL2c6nZjz/rPvfccwW/sIVKT0+3d999184880w788wzbdSoUXJXtsGDB9s555xjzZo1swsuuMAqVKhg06ZNs5kzZ9oDDzyw1+Nv377drrjiCrvzzjstNzfX7r77brvuuuusQoUKlp6ebtdcc43dfPPNVrt2bWvWrJk99NBDtnnzZrviiivMzOzaa6+1xx57zK6//nq77rrrLCcnx+6++2676aabCibGFi1a2OTJky03N9cyMjKsdu3a5XbSLKtWr15tF154oV1++eXWsWNHq1Gjhn355Zf20EMP2XnnnWetW7e2HTt22N/+9jfr27evTZgwwf7xj3/s9/MMGjTI/vSnP9lhhx1mhx9+uD3yyCO2bt26gn+vVauW1alTx/71r39Zo0aNbNGiRWwxfhBg/KE4FTbeQvzmN7+xZ555xgYMGGC33HKL1a5d2+bNm2evvfaaPfvss/s9lq6//nrbtWuXnXPOOfb+++9br169Ur6ul2klnGNSJEKSy5ctWxb169cvatSoUVS5cuWoefPm0eDBgwuSY3+6w9Aejz76aNS8efOCtkouf/TRR73neuaZZ6KmTZtGFSpUiE488cRozJgxUZMmTbx+I0aMiFq3bh1VrFgx9jxPPfVU1LJly6hSpUpRmzZtohdffDH2ODOLnnzyyah3795RlSpVohYtWkRDhw7d59+PonHOOefEEv1/avLkyZGZRdOmTStILl+7dm3Bv3/zzTeRmUULFiyIosgfcxs3box69OgRnXDCCVF+fr4c16NGjYp69OgRVatWLcrMzIy6du0a/etf/9rr+Q4cODA677zzosGDB0d16tSJMjIyoiuvvDK2y8eWLVui66+/Pqpbt25UpUqVqGfPntEXX3wRO864ceOiY489NqpcuXLUsGHD6NZbb4127NhR8O85OTnRcccdF1WrVi32N6L02Lp1a3TbbbdFnTt3jrKysqLq1atHbdu2je68885o8+bNURRF0SOPPBI1atQoqlatWnTGGWdEL774YmwcqzE5fPjw6KeXkh07dkSDBg2KMjMzo5o1a0Y33XRTdOmll8aSez/88MOoXbt2UZUqVaKOHTtG48aNi222QXJv+cP4Q3EKGW8mNvjJysqKXnjhhYL2nDlzov79+0c1a9aMqlWrFh1++OHRjTfeWLDZSipj6eGHH45q1KgRTZgwIYqiwq/r6jzLsrQo2s+bI7HfbrjhBtu5c6c99dRTRXK8tLQ0Gz58uPXr169IjgcAAAAk7aC51aoktW/f3tuFCgAAADiYsPAoBldddVVJnwIAAABQolh4lEHcHQcAAICyJuWFx+7du23p0qVWo0aNWGExHLyiKLKNGzdadnZ24jsaMf7gKs7xZ8YYRBzjDyWNazBKUuj4S3nhsXTpUorhQVq8eHFBfYekMP6wN8Ux/swYg9AYfyhpXINRkgobfykvPGrUqFHwBJmZmakeBuXIhg0brGnTpgVjI0llbfz9tICVmdlrr73m9flpocs9VD2PihXjH9vVq1d7fdR/gVITwYwZM2LtlStXen1WrVrlxd59910vVtKKc/yZlb0xGGLNmjVeTP1t7hgsLdzbUNVtqUn9l+CyNP72VGb+KfW6uP1CXztVAXzx4sWx9uzZs70+Xbp08WINGjQIes5ULVq0KNbOycnx+px22mleLNX/yh/62qfiYLwGp/p65ufnezE1JmfNmuXFjjzyyFi7SpUqXp9ly5Z5sfr163uxDh067PM8zfQ8Vhp/ZQodfylfPfb80ZmZmeXmoouiURwfiLI2/twFxJ4K9z+lJq+qVat6MfdLn3qceg+qVavmxdzzqFSpUqHPZ6a/jJYWxTUhl7UxGGLHjh1ejIXH/ikL468kFh7ul5Hq1asX2scs+bkm5LzUOZTGhcceB9M1ONXXU/VJT0/3Yuq66V7P1TVYHUv9h8SQ166sLDz2KOzcKC0MAAAAIHGl8z9bAeXMuHHjYu2ZM2d6fdR/JViwYIEXc38iVrdC1apVy4tlZWV5sZo1a8badevW9frk5uZ6MZRu6r+QjR49Otb+73//6/UZO3asF1u+fLkX27p1a6x99dVXe32++eYbL6b+66R7K8Phhx/u9Xn22We9WMeOHb2Y+xlSn6my9l8Pk6D+3lT/y/Gvf/1rL7Zt2zYv5v5XYTWuHn/8cS+mztX9Za5Tp05eny1btngx9Uvdd999F2urX11GjRrlxdatW+fFzj333Fj7/PPP9/qE/LK0t37whb5O7i10Gzdu9PrMmTPHi02fPt2LuddSdb1V48OdN838+ejoo4/2+pS3+YmRDQAAACBxLDwAAAAAJI6FBwAAAIDEkeMBFINNmzbF2oceeqjXR21lqvZJd+8Hbtu2rddH3WOt7iN2czzUlr7qWCrvo0WLFl4MRWvhwoVe7Oc//7kXc8ebmdn69etjbXVvtHr/1e4s7nm4OUxmOj9JcbdQVfde/+IXv/Bi6n7pq666Kta+7bbbvD7kfaS+29ftt9/uxdauXevFsrOzvZi705Wa29wxamaWl5fnxdzxcM0113h9unfv7sXU1rzuuao8N7Xbm9r9ys2bcrfqNTP77W9/68XU+4HUzZ8/34stWbIk1m7evLnXR401df1zx5G69h1yyCFerE6dOl7MzQX58ssvvT5qm+myjF88AAAAACSOhQcAAACAxLHwAAAAAJA4Fh4AAAAAEkdyOVAM3MJEK1eu9Pq4hQHNdJKwG6tfv77XZ+fOnV5MJUi6ibwqyVEd69NPP/ViJJcn77LLLvNiKrlXFbRyk8RVMrFKqFbHcjdHUEUsTz31VC+WmZnpxTZs2BBrZ2RkeH1Ck7/fe++9WHvEiBFen4kTJwYdqzwLLVj3/fffx9qq8KlKElcJue5rrJ6vcePGQcdyk7Zff/11r49K/laJ4+6Y3LVrl9dHnauKuYnqM2bM8Pqo46tEZLef6gNNFe9zE8LdgpZmZk2aNPFiL730khcbPnx4rH3WWWd5fU477TQv1q5du0LPS23coophVqtWzYuVFfziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkDiSy4Fi4CbfqurMIdWmzfzq0ipZUSXjquO7CZ8qkVMll6uEZhS9Z555JtZevny510clzIYmyLrUuFGbEmzevDnWVomOaryp8RWSRKtiVatW9WL16tWLtd3EdTOzYcOGebHzzz/fi5VnFSuGXfo/+uijWFuNIXcsmOn3Rs0jLjUvNmrUyIu5m3O88847Xp+jjz7ai6kNPNzEXfU3VqpUyYupBH3386M+O+PHj/diJ510UqHHgn7N3Q0QzPT7PHXq1FhbbYqgNjeYN2+eF6tcuXKsvX37dq/P0qVLvZja2MLdKEFVXVdJ7wMGDAjqVxrxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiSC4vAqpq6j/+8Q8vduSRR3oxt7rveeedV3QnhlLDTRJXCZMqqfG7777zYm5it0rkVEKSFVW1X/U4dV4oek899VSsrd4LlUiuuAmyocmrqqp3yGNVArM6VzdRUz1OVRlWycpu8qlKSleViA+25PJQ7uc8dNMK9z0104nBLvV+qcRddzyoavchjzPzE8DVPKzmWLXxx9atW2Nt9dlR1d9VcnnoBgAHE5VI7iZnm+nrWOvWrWPt6dOne326du3qxRo2bOjF3OriasMAdawvvvjCi7lJ7qeccorXR30uJkyY4MXatGkTa3fq1MnrUxrwiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsdNhEXg888/92KqENKUKVO82N/+9rdYe9CgQV6fxx57LPWTc6j7cR944AEv5hYq++c//+n1UUWVoIukuYXMVL6Pui9a3SO8bt26WPuHH37w+qgCSpmZmV7MvRdWFaNr0KCBF8vLy/NiSJ66T17dy67GoPteq/v1Q4oMmvnjUj1OjV1137rbLyR3w0zfd+8WMlSPc+/PNtPFvrKzs73YwcYtZqbeP1Ukzy3KZ+a/X2q+U+NIjVN3jKjzUo9T98q7j1XHUp8nda7u363OwS1+iHDutc/MrH79+kH93Hnm9NNP9/qoa6QqTuk+VuWuqVwNNbbcsbxmzRqvT3p6uhdTnzv3unzYYYd5fVQ+VHHjFw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDEkVz+EypBSCWjuVQhl6ysLC+mEs7dwkGPP/641+eSSy7xYsccc0yh56USrFShotWrV3uxzZs3x9oDBw70+px44omFnsPBSCWH1ahRI9auV6+e10clHarEYfe9UUm8KrmzZ8+eXsxNkFTjXSXxhhafQ7jLL7/ci7nvo/vem5ktXrzYi6kkSbcQliqmpsabGl8h4yaU+9jQgogqOXnZsmWx9qpVq7w+7mfRzOyTTz7xYgMGDAg6j/JCJau6iajuJhlm+n1QG164hdLUvKI2A1CbDbjUuFVUkniqY9ctFmjmz/3u32ymi+BBc+c/9T6rhG2VjO0eS11v1XvavHlzL+aOSVUssHHjxl7s22+/9WLuJj3qMxD6uXD7LVmyxOtz+OGHe7Hixi8eAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiSO5/CdUEqXiJpAtWLDA66MSeFRim5sE2rp1a69Ply5dvNgFF1zgxZo1axZrP/LII16fQw891Iu5SadmfhJhnTp1vD7Q1q5d68XcBExV9VYld6rETTf59rvvvvP6qKrLixYt8mItWrSItd3Kz2Y6UZmq9UXv+uuv92IffPBBrK3Gg9pIQI2lTZs2xdoqKVMl2obMi6qPiqlNCdyxpBJDVSKyW4ndzGzmzJmxtnpt1Hl9+umnXuxgSy53qx6b+ZsZqHkrPz/fi6kNNtq2bRtrq00E1PhQ/dzzUMm3oePPpeY2NS9+/fXXXswdu+pzqDZ9geZuDqHeZzU3qCTx2rVrx9rq+5iaL9T79eyzz+7z2Gb+Rhd7487pasyouVp9Xt1jLV++3OtDcjkAAACAgwILDwAAAACJY+EBAAAAIHEsPAAAAAAkjuTyn1CJc8orr7wSa9esWdPro5KgVIKQWzVcJUy6SXlmZu+//74Xc5NH27Vr5/VR1YrXr1/vxdykQlUBs3379l4MOrFNJcy6VFKZStysW7durK2SKNWYVEl4ubm5sbbaRECN29BKwQjXqVMnL+Z+7s4//3yvj0rkbdmypRdzNxdQ84qaA9W4CakmrZI31fzmHkt9VlTFbJXQ2aRJk0L7/Pa3v/Vixx57rBc72Khk6ZDPuXutMNPjw51H1Hynxp+KhW4EE/K4kMrlqo+aF91EZLVxi5pj3XnYzN/442DkXkvVtXXjxo1eTF3/QjZKUN+P1Jz19ttvx9onnXSS10e9f+q7lvtZUd8dVdK7Si4/+uijY+3QBPfixi8eAAAAABLHwgMAAABA4lh4AAAAAEgcOR4pePDBB2PtrKwsr4+6J1ndE+oWNFL3MKqiR02bNvVi7v2rNWrU8PqoewXV/avu/beff/6516dPnz5eDPo+YlWAyqXu7VRjSxUMdNWqVcuLZWRkeLHDDjss1lZFBtWYVGMLyRs2bFhQv1/+8pdebOXKlbG2ysFQ+RzqXmi3qJuaQ9Tj1Fzm3uOs5kn1+VF5Z6NGjfJiCKOKjbnUPfBubqGZLjrqXlPU+6zmQDVm3H6p5m6Y+QUD1fOpfBT1Wnz//fextsqjUsefOnWqFyPHw8+JUNcwleOh+rnF9dT8p6jvTKeddlqsrb6PqceFFDtURWJD8+Xcx4Z+n0w1ZypV/OIBAAAAIHEsPAAAAAAkjoUHAAAAgMSx8AAAAACQuIM2uTw0wWbBggVezC3KoooEqaQelUzn9lPnpR7nJnea+cWeVPEsRR3fTQydNGlS0LGg38OQ4pSqj0oqU0UFXa1bt/Zi06ZN82JucrlKWFNFj0ITN1EyQuYMlbAdWvxSjXGXGiMqediNqWOr+S6kiKGizkHN/cWdcFnS5s+f78XcpGqVHKsKWLZp08aLufNb6PsX8n6pY4WMUTP/b1RjTSUrq35uTI0h9ffk5OQUep7lnSpE6W7SoxKv1Xc0NWe5RQVDP/OqiKa7uUrIvGYWNo+p7wEqUX3VqlVezH2s2vjBLVpt5hclThq/eAAAAABIHAsPAAAAAIlj4QEAAAAgcSw8AAAAACTuoEkud5NzVNVRlVh03333ebF69erF2qpKa2iyUUgCnEpIUpVU3YQn1UfFVOKSm0w3bty4wk4T/z81jtxkX5XErZJ93WrTe+vnUsmdEyZM8GJusqjaKCEvL8+LqTGJ0sNNygyl3leVJO7OIyrpU801bpVos9QT1VUCaYiQjR4ORkuXLvVi7mYDboKumU4KVtdXN4k2NHk/1bkm1fdZnbtKMK5Vq5YXc8e8uuarzUHUHHuwCak+r5Kl1Vyn3sMQapOCkGTvkGuymX7v3flPbQwyZ84cL7ZkyRIv5o4/NUe6myOZkVwOAAAAoBxi4QEAAAAgcSw8AAAAACSOhQcAAACAxJXL5HKVrBiS/PPOO+94sSFDhngxtyq0Sm5SCUIh1dJDH6cqyLoJVSrpTyXJKW7i3Lx587w+o0ePjrVV4hd+FFJpV72nqp9K8HQdccQRQeflVjFVY83dTMHs4KvqXNaoatLuHBiaSKkSZEOqTodusOEmkKpkYpWUnmoCKTQ1ZtQGAS51zVKbZ7hCKzuHbD6g+qhrndqkwJ13t23b5vVR813I5gbq9du4caMXU4n9Bxv1WrnjSPVR80CdOnW8mHutU++pmtfUOHXfe/X9Uo0/NY+FzKXqu5W6LmdlZcXa7uYQe4sVN37xAAAAAJA4Fh4AAAAAEsfCAwAAAEDiEs/xUPdehsZc6v5jdZ9eyP3nf/zjH73Y/fff78UOP/xwL+bek6fuSw0tQhTyd6t7ANV9te69iOp+VhULyT1R+QfTpk2LtUvDvYOlQcj9xuq1UkXY1Jh3C0Uqxx57rBcLuVdfjQ91z3PIPdwoOatWrfJibqFTVThU3beu5jd3LIXm/ITkNqncNFWk1S1yigOjxoM7l6k+aqypcRRyL7uixpZ7XmqMqmuW4j5Wzbnq2q3mQHcOV/O8OhYFWfXn3n1d1FhQuUkheTrqOh3yvUrF1Dmoz4D6Xuj+3WosqPNShQCXL18ea6tcl9LwPY1fPAAAAAAkjoUHAAAAgMSx8AAAAACQOBYeAAAAABKXeHJ5qsnfB2LEiBFe7JZbbom1c3JyvD5HHXWUF1OJZm6CnUpyVIl0KjHPTRoKfb1UErCblBSaFKySmdwEOFUgx02oUs93MAopgqUKIa1du7bQx5mFFQcMKTJo5o/v0ARQCggWD/f9D33dVUKkm2y7fv16r48aN+pYIZtihCZJusdSybcqFpI8HLJxxsEodK52E1FVIvnRRx/txdQ4chNfVaKtem9CkntVYbaQ4odmYYU11evVoEEDL+YmFKvXKzSp2T1/9TeWJ+q1cj/3ak4J3ejE/c6kxof6vqc2fXGFzLdmuti0+5xqrlNJ4ur7pHse6hwWL17sxYobv3gAAAAASBwLDwAAAACJY+EBAAAAIHEsPAAAAAAkLvHk8lCrV6/2YmPGjIm1p06d6vUZOXKkF5s5c6YXa9OmTaytKjurZCOVwOMmG4UkWu6Nm8imkswUVRXTTYBTCejq+Cp5yj0v9doU5etQnoSMo7p163p98vLyvJhKamzatGmh56Cqm6vkRPd9VklyahyFJNyh5IRUR1YVbNUYCalorZI+1edAzT/u+FJjUH0OVCIowqiNLBT3fQ1JjjULS+xWjwt9T1O91oRUplafHTXfbdq0yYu5ic5z5szx+qhkfPWcK1asiLUbN27s9SlP1JhxXxf1eqr5omHDhl7M/V6oNgUKreodMk7VmNm4caMXq1WrVqz95Zdfen2ysrK8mNrcwN3AQX1OVBJ/cWPmBgAAAJA4Fh4AAAAAEsfCAwAAAEDiWHgAAAAASFyRJ5ePGzcu1r7vvvu8PqpyoptIZWaWnZ0da+fn53t9VJL18ccf78XciqgqmUtVTQ1JEApNRsvMzPRibkKVSpRSVcNVP/f8VfVYlQSqYm7ylHrtu3fvHmtv3rzZ64MfrVy5MtYO2RzATI+t1q1bp3QOKpnOfU411lTCnToWil6qlcvVe+bG1BylksvVZ989DzWHKKoqtJvwq85dJQWvWbOm0OejSrm2bt06L6Zed/c6o+b45s2bezE1l7nvobqGhSaNu+Mt9H1WG2W41LHUPKwqr7dv3z7WVt911OdOfX5U8np5puYe93UJreqt+rljN/Qapt4H971Xc6TaiEG99+73tAULFnh9jjjiCC/WtWtXLzZq1KhYu0OHDl4f9RmbPXu2Fzv88MO9WFHhFw8AAAAAiWPhAQAAACBxLDwAAAAAJO6AczxWrFgRKzB1zTXXxP5d3dOmiqepmHtPqCqioo6v7v1V99S71D2noQXVQqiiM+55qftL1b2IqtiOW4BOnbu6L1Xdtxty7/8JJ5wQa6t7Gg9Gany4RdiWLFni9VH3v6v32S2GGUrdC+ve660KD6oxyb3zpZu6797NTatatarXR72vagy6/dR9w+q+dZWrofIBXOqzoWIIE5qX6F4b1Ht1xhlneLHp06d7MfcefnV9UtdI9T6756GOpcafOpb7nKGFNdVreNhhh8Xa//3vf70+Kh8gtEBheaYKlbrXUjX+evXq5cVCvmuF5qWpOcud/0LnIvU91L0Gu2Nob9R3Zvcar8aVmuOLu6ggv3gAAAAASBwLDwAAAACJY+EBAAAAIHEsPAAAAAAk7oCTy5999tlYwrKbcKUSwlXyluImCKkCfCoBSyX3uv1Ugo1KIlJJSm6itXq+kGJMZmbVq1ePtVXCmipCtGzZMi/WsGHDWLtRo0ZeH5VgrBKK3b9JFZxKNVkL4YmDKmm3du3aKT1nkyZNvNisWbNibZVwrBLnVLIlip47Z6j5SI0RtWGEO7+FFOzam5BCb2ouU3OlO75Un9Ck4xDq3A+2zRLUtU5xXyv1OLVhidqQwJ23DiS53L32qMeFFmR1qWukOr66LruJzqqIr3pt1AYyB9tmLSoR2n1d1PVJzWNqbIVQ39tCNjVSifHq++oPP/zgxdxzbdmyZdDj6tWr58XcjQvUeG/atKkXC9l8qSjxiwcAAACAxLHwAAAAAJA4Fh4AAAAAEsfCAwAAAEDiDji5/JBDDoklnLlJs27ytJlOBlJJPW5Cl0qoDk0UdJOSVEKcSiALSYALOXcznezmJg2phLKTTjrJi91///1ebPTo0bG2em1CE0rdJKXirmxZ3rjjSCX/qoRz9X7VqlUrpXOoX7++F5s9e3asrTYRULHGjRundA4oeurzqz777px0IAnbbr/QZM6QfiqBWX021KYYCBOy+YCZf91U17DQ5HL3+q3mMZWku2bNGi/mzmWqj0pWVmNm9erVsfaiRYu8PipJXFUgd797qO8sHTp08GIqaVq9FuWZmrPceUYlbKvE/JBNh9RcpK63ak4M2RBDHV8dyx1b6jO2cuVKL6YSx7t27Rprq8+5uzmSWfHPpfziAQAAACBxLDwAAAAAJI6FBwAAAIDEsfAAAAAAkLgDTi6/9dZbYwk/bhLMxx9/7D1GJVyp6pBuco5KIlIJcSrZ2+2nEn9ULKSauXqcm7CmHmdmdtNNN8XaN954o9cn1EsvvRRrq8rl6lxDkgNDKr5i70IS21QimEqmU0mTIVRFXvdYaryr9z608jGSp+a7kM95SBXxvXGPrxLc1SYfIQmXaj5SY14lkIagcrn+TKsE7fXr18fa6hoWkmRt5o/T0I1O1Lm63zPcTTLMzI477jgvpjbYcP9udQ4bN270Yuq1aNiw4T7bZmaHH364F5s7d64XO9iuuWo+ct8LlXhdt25dL/bll1+mdA5q7lHjwZ2P1JyiNgpSmwioz49LffdVmyC0bds21v7000+9PupvVBvIJIlfPAAAAAAkjoUHAAAAgMSx8AAAAACQuCK/UfuJJ56ItVUhoccee8yLvfjii17MLa63du1ar096eroXU8VQ3HvyVBEVda4hRf/Use68804vdscdd3ixojR9+vRYW90DqO6jVLkF9erVi7WXL1/u9XHvQT3Y7kndG3XPvXuPpronVBWMys7OLrLzatGihRdz3zN1D6pCjkfxUOPEVZS5DqG5Gu797So3RB0rZNyE3FNtpucthFH3lYfca67e58mTJ3sxdd/9kiVLYm31nqpzUGPGHSPq+dR98er47rFULtzMmTO9mCqA+OGHH8ba6vuDyqVR992ra+7BTn3XUtR1zB27aiyrsaa+M7kxdSyVw6Su8e48pvKdVa6n+r7qFiNUc6mixl+S+MUDAAAAQOJYeAAAAABIHAsPAAAAAIlj4QEAAAAgcUWeIeoWiFIJNjfffHNQzKWKEX799ddeTCWCLVy4MNZWBVNU4pJKurnuuuti7dtuu83rkypVYEsVKlL+9Kc/xdrVq1f3+qiEPpWE5yYpHXPMMYU+f6qF7coblfTlJqOpRHyViOi+DwdCFc9yE4dVIrE6V5Vwh5LhFnkzC0sSDy2iqpLQ1bzuCk3edM81NCFSfc4QZsWKFV6sdevWXsy9TqpCeqpIntpgw72WqkRbNa7U+HOPr65hao4KmctUIq/awEElMLvHV+eVk5PjxdTn4mAraqm418RmzZp5fVShvu+++86LdejQIdYO3VwjZOMMNW7V+FAbBrifC/V9Tx1ffV8I2bwjtHBnkvjFAwAAAEDiWHgAAAAASBwLDwAAAACJY+EBAAAAIHFFnlwemgidilNOOSUoVpYdyOs3cODAIjwTpEolvYYk46pkN7VBQMjjVGKiSnYLSWxTyZahFc5xYFKtXB4yJkLnGpVw7gpNTlTJve7nRX1WQhPhESZ0cwt3fli1apXXR801aqMWN9FazSEh493MT3o/9NBDgx4XMseqcVWvXj0vpj4/7t8YmvSuNrEJSRQuT9TGBYsXL461jz76aK+Pu3GQmVlubq4XO+qoo2JtNWep11yNB/c9zM7O9vqsXr260MeZ+eNPJcur7wFqgwj3s6j+npUrVwadV5L4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxB1f2ElBCatWqVWgflUCmkjRdIclvZmZ16tTxYm7ymUqYDE1UR8lQyeWpVvWuXLmyFwtJHFfVfdUYUWM1ZCypcakSOt3kYao/a+np6V5MJfe2aNEi1l6/fr3XRyWr5ufnezF3flOPU++zOlc3aVslxqvK6Ir7d6vHhc6LixYtirXVRgkqpq4PoQnz5UX79u29mPsaZGVleX1UEvd5553nxTZv3hxrq40GVDK26uduBqDmTfVZqVGjhhdz52917VbfA9RGD+6mET/72c+8PupzHrL5TVHiFw8AAAAAiWPhAQAAACBxLDwAAAAAJI4cD6CIqXuX3WI/devW9fps3brVi4Xc/x6a46Hu43TvjVb3Lat8AXUPN4peSI6Cei/UfcnuPcFLly71+qh7qNX4co+vcjzUffEqX8T9bKjnU/e7z5w504u5heVCcqsORkceeaQXUzlm06dPj7UffPBBr4+6L17dd+/OeSqXYu7cuV5sxIgRXszNPVFjbc6cOV5MjQd37J5++uleHzUm3bFm5v+N6j7/L7/80ovVrFnTi/Xs2dOLlWeqUK2Kub7++uug46sijS6VL6e4403lTahrsDq+uu671Byv5lI3x6h169ZeH5VnUtz4xQMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxJJcDRaxDhw5erG/fvrG2SsatXbu2Fzv55JMLfT6VWKk0bNjQi7nJZyphsl69el5MJaei6KnEXVefPn282OjRo71Ybm5urK0KUKlER5XY6CZTuoWrzPS4VBsVuAntapy6BePMzFq2bOnFQpLJKSqoi7XdeuutXuyzzz6Ltc8991yvjyqeVpTuuuuuRI+fJJVcPmjQIC/Wq1cvLxby2T/YqOumShpXm7K4c1tIcV4zvSmLO4+p51Pvn9p4xr2+qgR0lWSvzj8kGV9tlBD6HaKo8IsHAAAAgMSx8AAAAACQOBYeAAAAABKX8k2EewpIbdiwochOBmXbnrGgipcVtdI8/tQ98e79pepeVXWfvLrf0/2bVSEhVbxI3b/vPqe6B1+dqyr+VdLvRXGOv58+T5J/t/u3hOYnqDHojqXNmzd7fdyCkmb6vXbHkho36lzVWHKPpZ5P3YOs/saQ90KNj6LI+yjr4y9kPKjnSjrHoyxTr5ea54tqPi3v12A1f6h5IGSeUddNJSTHQ73eKsdDXc/duUd9DkOP5ebeqdyTJHM8QsdfWpTiCF2yZIk1bdo0lYeinFu8eLE1adIk0edg/GFvimP8mTEGoTH+UNK4BqMkFTb+Ul547N6925YuXWo1atRglxCY2Y+r3I0bN1p2dnbiuyQw/uAqzvFnxhhEHOMPJY1rMEpS6PhLeeEBAAAAAKFILgcAAACQOBYeAAAAABLHwgMAAABA4lh4AAAAAEgcCw8AAAAAiWPhAQAAACBxLDwAAAAAJI6FBwAAAIDEHdQLj3vuuceOPvrovf77kCFDrGbNmgf0HJdddpn169fvgI4BAEkrbD40MzvppJPsxhtvLJbzAVxpaWn21ltv7fXfx40bZ2lpabZu3bpiOycA+6dMLzwmTZpkhxxyiJ199tklfSolji8EpVNaWto+/3fPPfeU9CmijCqJsfXmm2/a/fffv88+ubm5lpaWZlOnTpX/fu+999qvfvUrMyv8iyQOLitXrrRrrrnGmjVrZlWqVLGGDRvaGWecYRMmTAh6fI8ePSwvL8+ysrL22Y//IIi9WbZsmV1//fXWsmVLq1KlijVt2tT69u1rH330UZE9R4sWLeyxxx4rsuOVNRVL+gQOxHPPPWfXX3+9Pffcc7Z06VLLzs4u6VMCYvLy8gr+/9ChQ23w4MGWk5NTEMvIyCj4/1EU2a5du6xixdL3sdy+fbtVrly5pE8DP7E/Y6uo1K5de5//vn379kKP8fbbb9ttt91WVKeEcuT888+37du327///W9r2bKlLV++3D766CNbvXp10OMrV65sDRs23Ou/79q1y9LS0orqdFHO5ObmWs+ePa1mzZr2l7/8xTp06GA7duyw0aNH229+8xubPXt2SZ9i+RCVURs3bowyMjKi2bNnRxdddFH04IMPxv597NixkZlFY8aMiY455pioWrVqUffu3aPZs2cX9Ln77rujo446qqA9b9686NBDD41+85vfRLt37/7/2rv3uCirdQ/gD8qdGfCGAoKogAYq5l2xIktSSy1zd6zTSd2ZtSsv1dZdlqm5u1+O1dZs6yk5WpaVYmXKR/OShBhkgiiIaCgYkHkX8QL6nD/8OMd3rYd4I16Ygd/38+nzaT2smXlnZs16Zznvsx5esmQJBwQEGO539erV3KNHD/by8uIOHTrwnDlzuKKiosrjHDduHN955508Z84cbtWqFdvtdn7kkUf4woULjj7nz5/nyZMnc2BgIHt5efHAgQM5PT3dcD9btmzhPn36sKenJwcFBfHTTz/teNxx48YxERn+Kygo+IOvKFhNHU9Xx+jatWu5Z8+e7OHhwZs3b652PEjjMikpia/9OGdmZvLNN9/MNpuN7XY79+zZkzMyMhx/T0lJ4RtuuIG9vb05NDSUJ0+ezGVlZY6/h4eH89y5c/mBBx5gu93O48aNq/XXA2qPNCYkmzdv5j59+rCvry8HBARwXFwcHzx4kJn/fz5cunQph4eHs7+/P48ZM4ZPnz7tuH18fDxPnTrV0ZbGiToXxcfHO/oXFhayp6cnnzp1isPDww39wsPDHf3ee+897tixI3t4eHCnTp146dKlhudBRPzee+/x0KFD2dvbmzt06MCff/55jV47cA4nTpxgIuItW7ZU2YeIePHixXzXXXexj48PR0ZG8pdffun4+9U59cSJE8z8/5+LL7/8kqOjo7lp06biGN28ebPFzw5cwbBhw7ht27aGc+FVV8fUoUOHeOTIkezn58d2u53vueceLi0tdfTbv38/jxw5klu3bs1+fn7cu3dv3rBhg+Pv8fHx2vhrbFz2GX/wwQfcu3dvZmb++uuvOSIigi9fvuz4+9UJqF+/frxlyxbes2cP33jjjRwXF+foc+3CIysri4OCgvi5555z/F09mW/dupX9/f05MTGRDxw4wOvXr+f27dvznDlzqjzOcePGsc1m4zFjxvDu3bt5zZo1HBgYyM8++6yjz5QpUzgkJITXrl3Le/bs4XHjxnHz5s352LFjzMx8+PBh9vX15ccee4xzc3M5KSmJW7VqxbNnz2Zm5pMnT/KAAQN44sSJXFJSwiUlJVxZWVnj1xasUdXCIzY2ltevX8/79+/nY8eOVTsezCw8unTpwv/1X//Fubm5vG/fPv7ss884MzOTma9MjH5+fjxv3jzet28fp6amco8ePXj8+PGO21/94vnmm2/y/v37ef/+/da9MPCnmVl4VFRUcEBAAE+bNo3379/POTk5nJiYyIcOHWLmK/OhzWbju+++m7Ozs3nr1q0cFBRkmKukhYc6TtLT0x3/6FNSUuIYt8zM8+fP59tuu42ZmY8cOcJExEuWLOGSkhI+cuQIMzOvWrWKPTw8eMGCBZyXl8dvvfUWN23alDdt2uS4HyLili1b8uLFizkvL49nzpzJTZs25ZycnD/7UkI9qaioYJvNxk888QSfP39e7ENEHBoaysuXL+f8/HyeMmUK22w2xxiTFh4eHh4cFxfHqampvHfvXj516hT/x3/8Bw8dOtRxvrz2HwKhcTp27Bi7ubnxyy+/XGWfS5cu8fXXX8833HAD//jjj7x9+3bu1auX4R9XMjMz+f333+fs7Gzet28fz5w5k729vR3z7LFjxzg0NJTnzp3rGH+NjcsuPOLi4vjtt99m5isTVqtWrQz/anHtLx5XffPNN0xEfO7cOWb+/4VHamoqN2/enN98803DY6gn81tvvVUblMuWLePg4OAqj3PcuHHcokULPnv2rCO2cOFCttlsfOnSJS4rK2MPDw/++OOPHX+/ePEih4SE8Ouvv87MzM8++yx37tzZsLBasGCB4z6Y9S8E4HyqWnisXr3aETMzHswsPOx2OycmJorHMWHCBH744YcNsZSUFG7SpInjsxEeHs533XVXjZ4n1D0zC49jx4797r8oz549m319fQ2/cEyfPp379evnaEsLD3WcFBQUMBHxzp07tcdISEjg+fPnO9pExElJSYY+cXFxPHHiREPsnnvu4dtvv91wu7/97W+GPv369eNHH31UfG7gGr744gtu3rw5e3t7c1xcHM+YMYOzsrIcfycinjlzpqNdVlbGRMTr1q1jZnnhQUSOf3S56uqVCABX/fDDD0xEvGrVqir7rF+/nps2bcqFhYWO2J49e5iItKtUrtWlSxf+17/+5WiHh4fzvHnzauW4XZFLJpfn5eVReno63XfffURE5O7uTmPGjKEPPvhA6xsbG+v4/+DgYCIiOnLkiCNWWFhICQkJNGvWLPr73//+u4+blZVFc+fOJZvN5vhv4sSJVFJSQuXl5VXernv37uTr6+toDxgwgMrKyqioqIgOHDhAFRUVNHDgQMffPTw8qG/fvpSbm0tERLm5uTRgwADDtakDBw6ksrIyOnz48O8eMzi/3r17O/7fzHgw46mnnqKHHnqIBg8eTK+++iodOHDA8besrCxKTEw0jOMhQ4bQ5cuXqaCgQDwucC2FhYWG9/fll1+mFi1a0Pjx42nIkCE0YsQIeueddwx5IkRXkh7tdrujHRwcbJgvJWbHyenTp+m7776jkSNH/m6/3Nxcw/gnujLfqeN/wIABWvuPfEbA+YwePZqKi4vpq6++oqFDh9KWLVuoZ8+elJiY6Ohz7Tndz8+P/P39f3eMenp6Gm4DIGHmavvk5uZSWFgYhYWFOWIxMTHUrFkzx9xTVlZG06ZNo+joaGrWrBnZbDbKzc2lwsJCy47d1bjkwuODDz6gyspKCgkJIXd3d3J3d6eFCxfSypUr6dSpU4a+Hh4ejv+/+sX98uXLjlhgYCD17duXPvnkEzp9+vTvPm5ZWRm98MILlJmZ6fgvOzub8vPzydvbuxafITQmfn5+f6h/kyZNtEmyoqLC0J4zZw7t2bOH7rjjDtq0aRPFxMRQUlISEV0Zx4888ohhHGdlZVF+fj5FRETU+LjAeYSEhBje37/97W9ERLRkyRJKS0ujuLg4WrFiBXXq1Im2b9/uuN218yXRlTnz2vlSYnacrFu3jmJiYgwnbQCVt7c3JSQk0PPPP0/btm2j8ePH0+zZsx1//6Nj1MfHBwnlUK2oqChyc3P70wnk06ZNo6SkJHr55ZcpJSWFMjMzqVu3bqY23mgsXG7hUVlZSUuXLqW33npL++IUEhJCn3zyyR+6Px8fH1qzZg15e3vTkCFD6MyZM1X27dmzJ+Xl5VFkZKT2X5MmVb+UWVlZdO7cOUd7+/btZLPZKCwsjCIiIsjT09OwXWBFRQVlZGRQTEwMERFFR0dTWlqa4ctmamoq2e12Cg0NJaIr/6pz6dKlP/TcwfmYGQ+BgYF05swZOnv2rKOPtHVpp06d6Mknn6T169fT3XffTUuWLCGiK+M4JydHHMfYuaphcHd3N7yv1+5G1aNHD5oxYwZt27aNunbtSsuXL6/Vx746htT56Msvv6Q777zTEPPw8ND6RUdHa9unpqamOsb/VdcumK62o6Oj/9Sxg/OJiYkxzHW1AedLULVo0YKGDBlCCxYsEMfbyZMnKTo6moqKiqioqMgRz8nJoZMnTzrmp9TUVBo/fjyNGjWKunXrRkFBQXTw4EHDfTX28edyC481a9bQiRMnaMKECdS1a1fDf6NHjxYvt6qOn58fffPNN+Tu7k7Dhg2jsrIysd+sWbNo6dKl9MILL9CePXsoNzeXPv30U5o5c+bv3v/FixdpwoQJlJOTQ2vXrqXZs2fTpEmTqEmTJuTn50ePPvooTZ8+nZKTkyknJ4cmTpxI5eXlNGHCBCIieuyxx6ioqIgmT55Me/fupS+//JJmz55NTz31lGPB0759e/rhhx/o4MGDdPTo0Wr/lRKck5nx0K9fP/L19aVnn32WDhw4QMuXLzdcinDu3DmaNGkSbdmyhQ4dOkSpqamUkZHh+FL29NNP07Zt22jSpEmUmZlJ+fn59OWXX9KkSZPq4ylDHSkoKKAZM2ZQWloaHTp0iNavX0/5+fm1/mW9devW5OPjQ8nJyfTrr7/SqVOnqLKyktatW6ddZtW+fXvauHEjlZaW0okTJ4iIaPr06ZSYmEgLFy6k/Px8+u///m9atWoVTZs2zXDbzz//nD788EPat28fzZ49m9LT0zGGXdixY8folltuoY8++oh27dpFBQUF9Pnnn9Prr7+uLVj/rPbt29OuXbsoLy+Pjh49qv1iDI3TggUL6NKlS9S3b19auXIl5efnU25uLr377rs0YMAAGjx4MHXr1o3uv/9++umnnyg9PZ3Gjh1L8fHxjktOo6KiaNWqVY5/EP/P//xP7ftY+/btaevWrfTLL7/Q0aNH6+Op1q96zjH5w4YPH25IMrzW1eSgrKwsLcmMmXnnzp2GrWbV7XTPnDnDcXFxfNNNN3FZWZmYsJmcnMxxcXHs4+PD/v7+3LdvX160aFGVx3s1iW3WrFncsmVLttlsPHHiRMOuHefOnePJkydzq1atarSdLjNzXl4e9+/fn318fLCdrpOqKrn82jHKbG48JCUlcWRkJPv4+PDw4cN50aJFjuTyCxcu8L333sthYWHs6enJISEhPGnSJEfiODNzeno6JyQksM1mYz8/P46NjTVsSd3Yk99cjZnk8tLSUr7rrrs4ODiYPT09OTw8nGfNmuXYoEKdD5mZ582bZ9jmVkoul8bJ4sWLOSwsjJs0acLx8fH87bffcmhoqNbvq6++4sjISHZ3d//D2+kuWLCAExIS2MvLi9u3b88rVqz43ecPzu38+fP8zDPPcM+ePTkgIIB9fX25c+fOPHPmTC4vL2dmeTOCgIAAXrJkCTNXvZ2u6siRI475j7CdLlyjuLiYH3/8cQ4PD2dPT09u27Ytjxw50jFGqttOt6CggAcNGsQ+Pj4cFhbG8+fP1+bNtLQ0jo2NZS8vr0a5na4bs4mMGgAAgBqaMmUKVVZW0nvvvVcr9+fm5kZJSUmoPg0A4GKcr0QyAAA0KF27dtV2oQIAgMYHCw8AALDUww8/XN+HAAAATgALDwAAcCm4QhgAwDXVeOFx+fJlKi4uJrvdjj2ygYiufBk4c+YMhYSE/O72wrUB4w9UdTn+iDAGwQjjD+obzsFQn8yOvxovPIqLi1EICkRFRUWO+iJWwfiDqtTF+CPCGAQZxh/UN5yDoT5VN/5qvPCw2+2OB/D396/p3fwu9ef0P7Oq3rp1q6GtFnQhIho7dmyN77+2LF68WIt17dpVizljoubp06cpLCzMMTasVBfjr6auLRZ5lY+PTz0cSe2orKzUYu7uzneVZl2OPyLnGYM1vezI7HxaXFysxZKTkw3tkydPan2k2gg33XSTFjMzl0nPUTr+2jxn/FGNdfyB88A52LzPPvtMi3333Xda7NixY1pMndukwtMtW7bUYv3799diU6dO/d3jdCVmx1+Nvz1cndD9/f1dYuHh5+dnaEtfBJ3hwyMdl3rsRM5xrFWpi5N9XYy/mvLw8NBiWHjUnbr6suksY9DqhYd0UlXH8/nz57U+TZs21WI1nctcYeFR14/pLOMPnE9jPweb4evrq8U8PT21mHQ+V0nnQ+l23t7eWswVX7vqVDf+XK5yOQAAAAAAuB4sPAAAAAAAwHLOe72ESSdOnNBio0ePrraf9DPYrl27tNilS5e0mJqtf/nyZa3P8ePH9YMVlJaWGtpHjhyp9vGI5J/s0tPTTT0mWEu6rOrixYuGtvq+ExG1bdtWi5m5jEbKKZEufZH6qdevtmjRQusTHh5e7TGA8zNz+cWaNWu02KJFi7SYOk4CAwO1PtK8KFUu37dvn6H94IMPan1qeumI2Uu0AMD5SXOK2d27mjdvbmifOnVK6xMQEKDFgoKCtNjZs2cNbekS0gMHDmix9evXa7Hnn3/e0JbO3RJXntvwiwcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGA5p87xMHO92pNPPqnF9u7dq8WioqIMbWmrx4yMDC0mFchRt5ccNmyY1ictLU2LSdf+l5WVGdrS/sfSsebn52uxxMREQ3v8+PFaH6gfjzzyiKGt1kEgImrWrJkWk67j9PLyMrSlegnStbDS50kdy9LtpDoO4Dyk99XM+5+UlKT1Wbp0qRaTxpd6XbV6zTORvI99RESEFtu0aZOh3atXL61P9+7dtdifud4bAFyP2c/3/v37tZg6X0jzjFSPqE2bNtUeh5QLLOXhSvmSak25GTNmaH1eeeUVLWZm3nfW+dA5jwoAAAAAABoULDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLOXVyuUpKJszLy9NiUgLPb7/9ZmhLBa+kBCG1uBWRXixmy5Ytpm7n7l79yy0lA6nF54iIgoODtZiagITkcuexe/duQ1sqSiS5cOGCFispKTG01Q0KiOTPgL+/vxZTE+CkDRDAuUkbEJhJKpSKBaoFJYn08UZE1KFDB0NbKnr13XffaTGpSKa6ecG7776r9Vm4cKEW8/T01GKuklxZm5jZMAactYiYOk6l4zRbFE09V0vvc03v3+wxuHIBN2dU09ezoKBAi6lF+Yj0898vv/yi9amsrNRiUuFd9TtZeXm51kfamEi6f7Ww4bp167Q+UmHDZ555RouZKW7tDHNi/R8BAAAAAAA0eFh4AAAAAACA5bDwAAAAAAAAy2HhAQAAAAAAlnOp5PKnn35ai0nJt1LSoVrtWaoGLiXWSolFp0+fNrSl5F4pUUqK+fr6GtpSgruUuCkdv5r0vnLlSq3P6NGjtRhYr7S01NBu0aKF1kd9/4jkJHQ1Ka5jx45aH2ksS58LNZaamqr1AedW04TW6667Tot5eHhoMWnOUBMUpSq9gwYN0mLSRhknTpwwtNWNGIiITp06pcWkDUIaY3K5m5vb746B7OxsLSa9z9J5rHfv3n/u4K5hZpyaHcvS+a+ujwGJ5LXLzOv54IMParENGzZosVatWlUb+/XXX7U+0gZAUkK4uknGzz//rPWRPk/Sdzn1vG+z2bQ+ixYt0mLbt2/XYqtXrza0pfnPGRLOG/6sDAAAAAAA9Q4LDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJOnVyuJsGkpaVpfcwmHarJ5RIp+VtK+FUThSVSAk9ISEi1jykls0v3JSUpqbddsGCB1gfJ5fVDTaqVkiPNbpTQpk2bau9LSoiTEsjUpGApwe/QoUNaTKqMDq4lNzdXix0/flyLRUZGarE9e/YY2lKiujSepQq/6lxmt9u1PuqGHkTmkssbQ3Xp8vJyQ1LsZ599Zvj7V199pd0mNjZWi0nzw9atWw3tdu3aaX1OnjypxaT3KyoqytD+7bfftD7SeypRH1M6v0vPR9q8RT2OZs2aaX2kc7CZ7xTSWJM2WJDma/XzI71earL1mTNnqj0mV7J582ZD+/vvv9f6qOOKSH6/1A0VpO920vlWeg/V13ngwIHV9iEiOnz4sBZTE9ql+U895xPJ8/c///lPQ1uq4O4MG27U/xEAAAAAAECDh4UHAAAAAABYDgsPAAAAAACwnFPneKjXoknX940dO1aLZWRkaDH1uk3pGkDp+k+poIxa/E0tgEVEFBwcbOq+zp49a2hL199J+RzSY6oFu9RrfaFuSO/XkSNHDG3pWmkpV6OiokKLqdemSsUCpeuPpYJGqpYtW2qx4uJiLYYcj7qh5ihIOQtmr9n94IMPDO3Q0FCtT5cuXbSYNFeq85t0PbN03bp6zTYRUUxMjKEtPR+1MBYR0d///nctpl6jLR17Q8vxWLdunaEQbWZmpuHvL774onablJQULZacnKzF1Byw66+/XutTUFCgxaQChWqOplTkTSrqdvToUS2mFt6VckP27t2rxaT5Tb2tVHBRmmOlXBB13lVzZIiIjh07psWk11XNm1K/KxAR5efnV9vHlS1btszQlr5DSTkzEvVzL50jpXOw1E/9riiNd+m+/vrXv2qxoqIiQ3vfvn1aHyk3rnnz5lpMyvtwRvjFAwAAAAAALIeFBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDmnTi43Y+nSpVpMKpK3ceNGQ1tK5pKK90mJjmrCopSwJiUwSsm9aiKylDwlFWOaMWOGFnvqqae0GNQ9qQib+r5KiWFmiz+ZKVylJoUSyeNIPa6goCCtj1SkE+qGOo9IG2BIc9SmTZu02I4dOwxtKWFWmn+k+/f39ze0pTGibsJBRDRixIhq+0lFtqTY1KlTtdg777xjaEvH3tCKCgYHBxs2nFCTWn/88UftNunp6VosICCg2piULB0fH6/FfvnlFy2mnquHDh2q9Tl48KAWk5J0x4wZY2irm3cQyQm50tys9pMSdOPi4rSYdN5XE4OlTWCkz5j6eSLSCwZKGwKoycpmNhBxJermKtL8J809ERERWqymxRWlTTLUmHRc0pwibXig3pe0wYJU7FBKaFcT1Z0VfvEAAAAAAADLYeEBAAAAAACWw8IDAAAAAAAsh4UHAAAAAABYzqWSy6UqtFLy4MqVK7WYmrTWp08frY+UkHThwgUtpiYnSolF0rFKSY2qnJwcLSYlRamVW8F5SAmMaiKvVJFcIo0tldlkWamfelxSEptUORjqh5RoK9m2bZsWUystS5sUSEnBXbt21WJ5eXnV9pESXaWESLWCtVQJW62UTiRvlqB+9qQEd2luNvu6OqP8/HzD51h9D6WEU+n9OnDggBZTz5u7du3S+gwaNEiLlZaWarHIyEhDW6rgbbPZtFi7du20mEqtWE9EFBYWpsWk86v6ekmbzEjatGmjxb7++utq+0iv/f79+7VYRkaGoS19D1CP1eyxuwr13CN935OSsUNCQrSYOt9JSePSPCCdN9XzsjSnSGNS+iyq/ex2u9Znz549Wqxz585aTH3/1cr2RERRUVFarK7hFw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlnDq5XE3qkRKLpORbKUFITWCUEh+lJCIppiYSSYm8UrKRdKzq/Uu3QyJ5w6NWrCeSE28l6oYHUpKcNGaksax+VqT7unjxoqnjgtqnvmdmK2xLiddSTCUl90oJsoWFhYa2VCVaOlZpowK1urM0z0vHLo3LzMxMQ/uWW27R+jS05PLmzZsbPu9qFe+goCDtNlIiufS61PS+Vq9ercV69+5taEuJtt27d9dimzZt0mLqBgTdunXT+qjJ2URyBfItW7YY2uomDEREP/30kxaTxox6jpcqsasVyYnkpHD1OKT5W92kxOymJa7CTLVxaR6QNjdQvwNKyd9mNnMh0jdlkc6b0n1Jj6nGpLEgfV+Q5le1n7RZCJLLAQAAAACgUcDCAwAAAAAALIeFBwAAAAAAWM6pczzMXM9s9ppnqViWSro+Uiog6O3tbWibKTBj9jHd3Wv+ltT0enCoXdI1p+p169L7LF1fL13bqV77KxUSSk9P12L+/v5aTB0j0vX1rnz9u6tTr7uX3gvpmnEp56J9+/aGtnT9b4cOHbSYdK28Om5KSkq0PtJ199J1/S1btjS0peulpQJaUr5Bdna2oS3leDS0ebG8vNww96vv4Y033qjdJjk5WYtJ15ZHR0cb2tIcIhVwe+KJJ7SYmqsh5fts3LhRiw0cOFCLqc9JGsu33367FsvKytJiubm5hvZ9992n9Rk6dKgWk/I31ByV7du3a32kArOSmJgYQ/u6667T+qj5Vw0tH1QtVBoYGKj1kb6jSdTvR9LtpO+A0nyhnifN5kFKc5t6XNIcb7YgsEqdD4mIbr755mpvZzX84gEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALCcUyeXm2E2UVAtziYVcpESf8wUdZMSi6TEHymhWE0ebWjJYY2RVJxSGm8qKalMGpPqRglS4S8puVgqjKWOb7NFOqFumEkg/Oqrr7SYlISpbkIgzUdSwqWa5EqkF5aTxryURCvNb+pmHVLRsLNnz2oxKdFZKhym+jMbeDijI0eOGF5DNVlfLapIJBdklM51p06dMrSl11dK2L711lurvX81cZiI6M0339Ri0phZtmyZoS0ll//1r3/VYlJi7ebNmw1tabMOKfH+iy++0GInT540tCMjI7U+0oYhxcXF1T6m9DlUPyvS59BVSPOF+nxCQkK0PtKcJZ2z1DlEeh+kOUXqp96/dN6Uzt0SNaHdzOY0RPJ3XzW2Y8cOU8dQ1/CLBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHIulWUnJVrWtAqtlECmJtIRyYltatKQlNwrJTdJycNqv4CAAP1gwaVIyWhqQqvZCuFSglqrVq0MbSnhTiJVZVVJibdSojrUDTPzm1S5XJort2zZYmhLYzA8PFyLqQmzRHqSsd1u1/pIVXOlDQ7U5yglmUrzop+fnxZTk1GlxFN1cwZXd/311xtei9WrVxv+LiU4BwcHa7HvvvtOi6mbCEgVyaXK5a+99poWU1/3N954Q+sjVaN/5513tJha9VzavCMtLU2LjRgxQotNmTLF0FY/J0RyUr1apZxI/17x9ddfa32Kioq0WNeuXbWYmmQsJfH379/f0JY2YXAVhYWFWkz9bmX2+550rlM3N5DOt2Y3nlDnTmm+lb4XmrkviXRcZjYekV5TZ4BfPAAAAAAAwHJYeAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJZz6uRyNXmmponkEqk6rpRsJCXwqIlsUrVQKdlSShRWk9elRKMTJ05osebNm1d7rLX5eoF5UuVRM6TKwWbGnzQWfHx8anQMkpo+H/jzpDlJtXv3bi3Ws2dPLaYm9+7bt0/rIyXphoaGajF1bpESbW02m36wgrCwMEP78OHDWh9pwwbptVHnz/z8fK2PlMjrynx9fQ3J5evWrTP8vUuXLtpt7rvvPi127NixamPqe0VEtHz5ci0mVUY/dOiQoa0mRhMRRUREaLEHHnhAi61atcrQlhJ5pc9AQUGBFlM3IJDOt9K5VHq9evToUW0f6f6HDRumxZYsWWJoS58B9fxgJuHYWUmbFKjnROl9MLvpkBqTvmtJmwJJMTOvs3QM0nuoPkfpfC5tbqBWtpceU5rPnQF+8QAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACzn1DkeZnIUpGs7pWv3PvjgA0Nbum5PKm4lXUen3r/0eFLBF6mojZrjIV3LN2PGDC32/vvvV3tcUD+ksSUVMlNJ413Kr1CvCZUKokn5RGaKWkpj1MyxQ92Qrg+Xcimk66XVgn5SXoZUwO3nn3/WYuo1zlKRyTZt2mgxqdihet29lDMnjd29e/dqMXUOzMjI0Po0tByP/fv3G3K61NwG6byQk5OjxW688UYtps4HqampWp/Y2Fgt5u/vr8Vyc3MN7Xbt2ml9PvroIy2Wl5enxdRCgNKY+f7777WYlEd3/fXXG9pSflxgYKAWk4oLf/PNN4Z2p06dtD5PPvmkFpPyrdQxL50f1HwoVy72Ks0N0nnMDOl9Vl8/s0Wepc9PTfNnpe+F6nFI48pMfo90XFJRbGeAb6oAAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLOXVyuRlmk3w2btxoaEtJRFKykURN9JGKtEhJulIivBq7thDUVTt27DB1XOAcpHGkvs9SYpiUxCYldqsFhqSEYDNJ6VUdh0oay1A/pPdVKtZ22223abEjR44Y2tLYkooFSptuqMnr+/fv1/pICZFHjx7VYuHh4Ya2mcJYREQxMTFaTC3YJm3W0dBEREQYzhvq6yfND507d9Ziy5Yt02LqaxwdHa31efHFF7XYgAEDtJj6Xqxdu1brIyUYFxUVaTE1mdzb21vr8/HHH2uxO++8s9rjKiws1PpIyfIlJSVabOTIkYa29BlLSkrSYv369dNivXr1MrRXr16t9VGT16Uke1chbZwhjV2VVHBPup06H5kttih9b1O/P5r9Hir1U+9fGjN9+/bVYsePH9di6vnh5MmTpo6rruEXDwAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOUaTXK5mgwp3U5KCpYqTapJSVLCp1RNWnpMM0lKUlKwGWaruoP11PdQep+l90ZKFmzbtq2hHRkZqfWRxqR0/2fPntUPVlHT6rFQ+1auXKnFpMrl0vuvvtc//PCD1mfdunXV3o5IT/idMWOG1mfFihVaTKoKrW6eIVXbHTx4sBY7c+aMFvvll18MbTVxvSGqqKgwbAChViCXkvw3b96sxX788UctFhISYmhLSdwdO3bUYlK1cZU0B95yyy1aTNo8QU1Cl8633bp102JSkq6ajC8l90qbIkjfF8LCwgzt/Px8rY+UXC4l1Y8aNcrQVhPXpduZmc+dlbSphPpeSGMmICBAi0mV5tUxKVUIl74zSUnoasxsdXOpn/odUHodoqKitJj0uVA/68567sa3UAAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOVcKrlcSvIxm1yuVhmVksalJDypkriaVGY2iUg6fvW+pIrqUhIREsedl5ScqI4Rafx5enpqMSk5TE34bN26tdZHSjKUNilQx5vUB5XLnUd5ebkWk5LLd+/ercWCg4MN7Z07d2p9pLlMSsJUk4yl6sHSWJISJ9U5UJonpcrobdq00WJqQrGU3NvQlJaWGt4jtcq2dF6QqtFLydjqfS1dulTro27cQkTUokULLaZuLJCamqr1kc5/UlVvtWK3NK4mT56sxdSNDIj0avc9evTQ+kjJ3wcPHtRimzZtMrSHDRum9enZs6cWkypMq+d4NXGdyHz1bVcgbVygzg3S+fC6667TYi1bttRi6kYtUlK6tGGAmWrj0mfMbEy9f+l8q87dREQZGRlazMx3U+n+a7qBUU3hmyoAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGC5RpPjoV7zbvb6PulafDPHJZGO1czxS9d1S0W2pOusoe5JOR7qGDGT70Mkv892u93QlnI8pOs4zXx+pGtopfEH9UN6L6RigVK+2t69ew1t6Xp6s3OZOi9KtzNbjMvMHCgVcJOueVfz4aQCnA2N3W435CIePnzY8PfS0lLtNr1799Ziau4YEdGBAweq7dO+fXstJuU/qEX+Bg0apPWRxrJ0Df/x48cNbSmnRMo9ke5fzQc4dOiQ1ke6fynHSM3VkPJYOnfurMVuv/12LbZv3z5DW/oM3HHHHYa2K493M/kIUh8pX8lMXob03c5s7qx0rjZDui/1OKT8TClnRSqaqRZVlfJmiouLtVhdF1rFLx4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLuVRy+Z/Rtm1bQ1tNwiGSk42kJCUzicJSkq6Z+zJb8EVNriNCcrmzkMaD+h5K40MiJQtKyZwqtfAXkZxwrBaHk5LrappIB7VPSgyMi4vTYlJBqOzsbENbmlfMzoEqacybTThXY1LSu3SsauE3Ir1AnJSoKcWkQrGuokmTJoakVXVzi7S0NO02UmFF6b1Rk6VHjRql9ZHmo23btmkxtUChVLBQ2phj8eLFWkwd361atdL6SHPn0KFDtZiaaP/aa69pffbs2aPFJk6cqMW6d+9uaL/yyitaH7WYMZH8fUTdJCAqKkrro26mII1tV2Em2Vs6F0nF9cx8/5IeT5pnpPmouuMkqnlRQakYpvTdLjo6WoutW7fO0FaLdhIRnThxQoshuRwAAAAAABocLDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADLuVRyudmqulICkpqwIyVMmk0IN1Pt2Sz1WM1WE5YS1CIiImp8HGAt9X2WxoyUWCklwHXs2LHax5Mq7UrJllLVV3AeauV66T2U5ge14jSRXPm4tphNLpeoiZlSYryaREskJ2EOHjzY0F6/fr3WR3oNXTm5vHXr1mSz2RxtNaFUSkKV5ho1kZxIr6gdHx+v9dm5c6cWGzBggBZT563y8nJTxyUlr6tVyaX3VLqv3377TYvt3r3b0O7SpYvWR6ocLVVGLygoMLSlc7KUiCyNb/V7xrXvcVXHJVWzdhWenp5aTH0NpPdU3TiISN48Qb1/KdFb+r5npp90XNJ9SXObev/S91fpvqTPhZocLx27ek6pD/jFAwAAAAAALIeFBwAAAAAAWA4LDwAAAAAAsBwWHgAAAAAAYDmXSi43U0GXSK7QbKbauJT0ZTZBsqa3U/tJyUDScUmJleAcpAQyNZlcSgKUqtdKyeVmEmGlpHEpAVNNZJMqt0pjEuqGmgwrJVLm5eVpMWnzArXC7969e7U+AQEBpo5LnYvNJmWaiUnVl0tLS7WYdPyBgYGGtpT0mZOTo8XatGmjxVxFfn4++fr6Otqffvqp4e8hISHabex2uxaTqn8vX77c0JY2LZAqkKtJ1kR6Je7bbrtN6yMlqksV6qVEa5VUoXn//v1aTE3QlqqUS3OulHCemZlpaO/atUvr4+/vr8WkMa/O/VLC9Pbt2w1tV/5eIJ3r1Hnl4sWLWp+wsDAtpr4PRPqmC9L3KrPfMVXSsUukxHH1+4JUbVz6biCRnpPKyk1GzMI3CgAAAAAAsBwWHgAAAAAAYDksPAAAAAAAwHIuleNhlplrBaVr7cwW7zOTv2G2GKF6TZ7Za6W9vb2rPQaoH9deb32VOmak4lnSey9diy2NU5V6PT+RfE28em2xNLbNXr8KtU+9vr2oqEjrIxWUjIqK0mJJSUmGtpRnZLZ4lZnbmb2GWi1cJxVdk56P9NlQr4+W8q1qmrfnrOx2u2HOUXMnpJxHtWgekTwe+vXrV20faS6TCuKp78WOHTu0PmZz01TSWJAKAUrnZakYr0oqFnjw4EEtpn4O2rVrp/WRclak4nlqgTipYFznzp0NbSlXxFVI+VgqaZ4xO4+ZOY9J51ZpzKhziHQ7aa4zM/dIOR7S7cwcq/TamPk8WQ2/eAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACzXILNGpYQaNbFISrirafEYSU3vy2zio1SEqKb3Bdbr0KGDoS0V6pOKP0mJw2ZIheakwljqOJUS8LCRQf1RCwhKybdS4qs0ltSkRSnJ0OycYaZQlURKiFTva/z48Vqf4cOHa7GEhAQtJiXzqqRkUVd2+vRpw3NSiyhKc823336rxXr06KHF+vbta2hLRQZTUlK0mFSIUk1Clwr8jRo1SotJSeiFhYWGtrQBi9nCieqGDdL3B+k1lD6LaqE3NfmbSH5t1q1bp8VuvfVWQ1sqnqcmuLtyAUEpMV7dkMBscVEzRXYlNd1MSPq+Zza5XI1JG2JInxVpLlXHrrRpgfTdt67hFw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlGmRyuURNujl16pTWR0pQqymzlSbVCptSxU3puKQkP1VtJsuDeQUFBVpMrVbbokULrY9awZmIKC4urkbHICXZSmNLTTRTkyOJ5Aq9UDfURFTpfZWSBaWxpL63ZhMipfmndevWhnZxcbHWx2wVanUumzdvntbnueee02Ldu3fXYpGRkYa2lEQtzf2u7LrrriObzeZoqwm40oYR99xzjxaT5oecnBxDOzg4WOsjxaT3Zs2aNYa2mgRPJG+UIG2k0rVrV0O7ZcuWWh8pIVz6rKgbcUjPRzou6Xyujnk1cZ1I/+wQEUVHR2uxw4cPG9rSeWXMmDGGtitXLpe+M6mbAaibChDJ41ZKLr/2M0Ikz2vSeypRN8SQ7svs/KqSxsfp06e1mDRm1GRy6fHMVIi3Gn7xAAAAAAAAy2HhAQAAAAAAlsPCAwAAAAAALIeFBwAAAAAAWK7RJJdLyY8qX19fLVbT6t9mb6cm/0hJSlJyoHSsNT0GqF1StVq1cnlQUJDW5+eff9Zi119/fY2OITY2Vos1b95ci6nJy1Ki3pAhQ2p0DPDnqZWIpSRDqaqtlECtJqZLiZpSUro0JtQqysePH9f6qBsqVHWs6vwmVds1WwE6Ly/P0JYqnte0qrGz6tKliyEBu1u3bvV4NFUbO3ZsfR9CgyfND65MTS5Xk7qJiCIiIrTY+vXrtZg6J0oV6isrK7WYNP+p/sxGPmpCu3QM0neD+Ph4LabOpdJ9qXN3fcAvHgAAAAAAYDksPAAAAAAAwHJYeAAAAAAAgOVcKsfjz1xHpxYYKi0tNXU7qeCVGpMKskgxKVdD5ePjo8XMXGMoQQHB+iFdVy7FrCRd/7llyxYtZrZgEtQP9TrkHTt2aH2kYpShoaFabPny5dU+XlZWlhaT8uPU/I0uXbpofUaMGKHFpLlMvY5bui+1MGBV93X33Xcb2tKx9+rVS4sBQP3y8PDQYocOHTK0pRwPNX+SSM5LTElJMbSl71rS/UsxNX9WOo+aLUit9pPy86RCwlFRUVpMLZAp5d4dPXrU1HFZCb94AAAAAACA5bDwAAAAAAAAy2HhAQAAAAAAlsPCAwAAAAAALNdoksvbtm1raJ85c0brIxXlkxI31QJXZ8+e1fpIiVJSQT81sUgq4KUmDBGRoWAUOBepQI9UFK2m1PEgbVogxcwkkksJu1IRIqn4EtQ+dZOAt99+W+sjzTVvvPFGjR6ve/fupmJm9OjRo0a3M0saz+o8L83fCQkJlh0TANSMtAHLt99+a2hLid6tW7fWYo8++qipWEMzcuRIQ1s6n48ePbquDqdK+MUDAAAAAAAsh4UHAAAAAABYDgsPAAAAAACwXI1zPK7mK5w+fbrWDsZKFRUVhrZ07ZvZ69vVXBMzfYjM5XhI9yUdl/S6q9c8S9dDmi1qUxNXj0l6nrXNmcefs+Z4mOHKOR51Of6ufZy6HIPqPEYkjzdn/FzUBfW1qMvXpjGMP3BurnwOlvJu1flO+k4jPb6V33OcmTrfSa+XVBS7tt5Ds+PPjWs4Qg8fPkxhYWE1uSk0cEVFRWLl5NqE8QdVqYvxR4QxCDKMP6hvOAdDfapu/NV44XH58mUqLi4mu93+p3abgoaDmenMmTMUEhJi+b84YPyBqi7HHxHGIBhh/EF9wzkY6pPZ8VfjhQcAAAAAAIBZjfNCOAAAAAAAqFNYeAAAAAAAgOWw8AAAAAAAAMth4QEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGC5Rr3wGD9+PLm5uZGbmxt5eHhQmzZtKCEhgT788EO6fPlyfR8eNABXx1dV/82ZM6e+DxHAobS0lCZPnkwdO3YkLy8vCgsLoxEjRtDGjRtr7THat29Pb7/9dq3dHzQ8156b3dzcqGXLljR06FDatWtXfR8aNHCYA63XqBceRERDhw6lkpISOnjwIK1bt44GDRpEU6dOpeHDh1NlZaV4m4qKijo+SnBVJSUljv/efvtt8vf3N8SmTZvm6MvMVY65+nbx4sX6PgSw2MGDB6lXr160adMmeuONNyg7O5uSk5Np0KBB9Pjjj9f34UEjc/XcXFJSQhs3biR3d3caPnx4fR8WNGCYA+sIN2Ljxo3jO++8U4tv3LiRiYgXL17MzMxExO+99x6PGDGCfX19efbs2czMvHr1au7Rowd7eXlxhw4deM6cOVxRUcHMzJcvX+bZs2dzWFgYe3p6cnBwME+ePNnxGAsWLODIyEj28vLi1q1b8+jRoy1/vlC/lixZwgEBAY725s2bmYh47dq13LNnT/bw8ODNmzfz+fPnefLkyRwYGMheXl48cOBATk9Pr/J+mJmTkpL42o9zZmYm33zzzWyz2dhut3PPnj05IyPD8feUlBS+4YYb2Nvbm0NDQ3ny5MlcVlbm+Ht4eDjPnTuXH3jgAbbb7Txu3Lhafz3AuQwbNozbtm1rGAdXnThxgpmZDx06xCNHjmQ/Pz+22+18zz33cGlpqaPf/v37eeTIkdy6dWv28/Pj3r1784YNGxx/j4+PZyIy/Aegks7NKSkpTER85MgRZmb+xz/+wVFRUezj48MdOnTgmTNn8sWLFw23+ec//8mBgYFss9l4woQJ/PTTT3P37t3r6FmAq8EcWDca/S8ekltuuYW6d+9Oq1atcsTmzJlDo0aNouzsbHrwwQcpJSWFxo4dS1OnTqWcnBz697//TYmJifTSSy8REdHKlStp3rx59O9//5vy8/Np9erV1K1bNyIi+vHHH2nKlCk0d+5cysvLo+TkZLrpppvq5blC/XvmmWfo1VdfpdzcXIqNjaV//OMftHLlSvrf//1f+umnnygyMpKGDBlCx48fN32f999/P4WGhlJGRgbt2LGDnnnmGfLw8CAiogMHDtDQoUNp9OjRtGvXLlqxYgV9//33NGnSJMN9vPnmm9S9e3fauXMnPf/887X6nMG5HD9+nJKTk+nxxx8nPz8/7e/NmjWjy5cv05133knHjx+n7777jjZs2EA///wzjRkzxtGvrKyMbr/9dtq4cSPt3LmThg4dSiNGjKDCwkIiIlq1ahWFhobS3LlzHf+aDVCdsrIy+uijjygyMpJatmxJRER2u50SExMpJyeH3nnnHVq8eDHNmzfPcZuPP/6YXnrpJXrttddox44d1K5dO1q4cGF9PQVwcpgD61B9r3zqU1W/eDAzjxkzhqOjo5n5yi8eTzzxhOHvt956K7/88suG2LJlyzg4OJiZmd966y3u1KmT9i8wzMwrV65kf39/Pn36dC08C3AVVf3isXr1akesrKyMPTw8+OOPP3bELl68yCEhIfz666+L98Os/+Jht9s5MTFRPI4JEybwww8/bIilpKRwkyZN+Ny5c8x85RePu+66q0bPE1zPDz/8wETEq1atqrLP+vXruWnTplxYWOiI7dmzh4nI8IucqkuXLvyvf/3L0Q4PD+d58+bVynFDwzRu3Dhu2rQp+/n5sZ+fHxMRBwcH844dO6q8zRtvvMG9evVytPv168ePP/64oc/AgQPxiweIMAfWHfziUQVmJjc3N0e7d+/ehr9nZWXR3LlzyWazOf6bOHEilZSUUHl5Od1zzz107tw56tixI02cOJGSkpIc1+8nJCRQeHg4dezYkR544AH6+OOPqby8vE6fHziPa8fWgQMHqKKiggYOHOiIeXh4UN++fSk3N9f0fT711FP00EMP0eDBg+nVV1+lAwcOOP6WlZVFiYmJhrE7ZMgQunz5MhUUFIjHBQ0bM1fbJzc3l8LCwigsLMwRi4mJoWbNmjnGZllZGU2bNo2io6OpWbNmZLPZKDc31/GvfQBmDRo0iDIzMykzM5PS09NpyJAhNGzYMDp06BAREa1YsYIGDhxIQUFBZLPZaObMmYZxlpeXR3379jXcp9oGuApzYN3BwqMKubm51KFDB0db/emtrKyMXnjhBcfEmJmZSdnZ2ZSfn0/e3t4UFhZGeXl59N5775GPjw899thjdNNNN1FFRQXZ7Xb66aef6JNPPqHg4GCaNWsWde/enU6ePFnHzxKcgfSz7u9p0qSJNkmqGx7MmTOH9uzZQ3fccQdt2rSJYmJiKCkpiYiujN1HHnnEMHazsrIoPz+fIiIianxc4LqioqLIzc2N9u7d+6fuZ9q0aZSUlEQvv/wypaSkUGZmJnXr1g2bE8Af5ufnR5GRkRQZGUl9+vSh//mf/6GzZ8/S4sWLKS0tje6//366/fbbac2aNbRz50567rnnMM6gxjAH1h0sPASbNm2i7OxsGj16dJV9evbsSXl5eY6J8dr/mjS58rL6+PjQiBEj6N1336UtW7ZQWloaZWdnExGRu7s7DR48mF5//XXatWsXHTx4kDZt2lQnzw+cV0REBHl6elJqaqojVlFRQRkZGRQTE0NERIGBgXTmzBk6e/aso09mZqZ2X506daInn3yS1q9fT3fffTctWbKEiK6M3ZycHHHsenp6WvsEwSm1aNGChgwZQgsWLDCMq6tOnjxJ0dHRVFRUREVFRY54Tk4OnTx50jE2U1NTafz48TRq1Cjq1q0bBQUF0cGDBw335enpSZcuXbL0+UDD4+bmRk2aNKFz587Rtm3bKDw8nJ577jnq3bs3RUVFOX4Juapz586UkZFhiKltgKswB9Yd9/o+gPp24cIFKi0tpUuXLtGvv/5KycnJ9Morr9Dw4cNp7NixVd5u1qxZNHz4cGrXrh395S9/oSZNmlBWVhbt3r2bXnzxRUpMTKRLly5Rv379yNfXlz766CPy8fGh8PBwWrNmDf3888900003UfPmzWnt2rV0+fJl6ty5cx0+c3BGfn5+9Oijj9L06dOpRYsW1K5dO3r99depvLycJkyYQETkGFPPPvssTZkyhX744QdKTEx03Me5c+do+vTp9Je//IU6dOhAhw8fpoyMDMdC+umnn6b+/fvTpEmT6KGHHiI/Pz/KycmhDRs20Pz58+vjaYMTWLBgAQ0cOJD69u1Lc+fOpdjYWKqsrKQNGzbQwoULKScnh7p160b3338/vf3221RZWUmPPfYYxcfHOy7Li4qKolWrVtGIESPIzc2Nnn/+ea0mUvv27Wnr1q107733kpeXF7Vq1ao+ni44uavnZiKiEydO0Pz586msrIxGjBhBp0+fpsLCQvr000+pT58+9M033zh+0b1q8uTJNHHiROrduzfFxcXRihUraNeuXdSxY8f6eDrgAjAH1pH6TTGpX+PGjXNsZ+bu7s6BgYE8ePBg/vDDD/nSpUuOfkTESUlJ2u2Tk5M5Li6OfXx82N/fn/v27cuLFi1i5ivJvv369WN/f3/28/Pj/v3787fffsvMVxJ54+PjuXnz5uzj48OxsbG8YsWKOnnOUH+qSi6/uk3fVefOnePJkydzq1atxO10ma+Mr8jISPbx8eHhw4fzokWLHMnlFy5c4HvvvdexlXNISAhPmjTJkTjOzJyens4JCQlss9nYz8+PY2Nj+aWXXnL8vbEnvzVWxcXF/Pjjj3N4eDh7enpy27ZteeTIkbx582Zmrn4ryYKCAh40aBD7+PhwWFgYz58/n+Pj43nq1KmOPmlpaRwbG8teXl6NcitJqN6152YiYrvdzn369OEvvvjC0Wf69OncsmVLttlsPGbMGJ43b5626cbcuXO5VatWbLPZ+MEHH+QpU6Zw//796/jZgCvBHGg9N2YTGTUAAAAALiwhIYGCgoJo2bJl9X0oAI1Wo7/UCgAAABqW8vJyev/992nIkCHUtGlT+uSTT+jbb7+lDRs21PehATRq+MUDAAAAGpRz587RiBEjaOfOnXT+/Hnq3LkzzZw5k+6+++76PjSARg0LDwAAAAAAsBy20wUAAAAAAMth4QEAAAAAAJbDwgMAAAAAACyHhQcAAAAAAFgOCw8AAAAAALAcFh4AAAAAAGA5LDwAAAAAAMByWHgAAAAAAIDlsPAAAAAAAADL/R8qgnO1GDUKvgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## 构建模型\n",
    "\n",
    "构建神经网络需要先配置模型的层，然后再编译模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### 设置层\n",
    "\n",
    "神经网络的基本组成部分是<em>层</em>。层会从向其馈送的数据中提取表示形式。希望这些表示形式有助于解决手头上的问题。\n",
    "\n",
    "大多数深度学习都包括将简单的层链接在一起。大多数层（如 `tf.keras.layers.Dense`）都具有在训练期间才会学习的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:25.659517Z",
     "iopub.status.busy": "2022-08-31T04:53:25.659003Z",
     "iopub.status.idle": "2022-08-31T04:53:28.866023Z",
     "shell.execute_reply": "2022-08-31T04:53:28.865360Z"
    },
    "id": "9ODch-OFCaW4"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "该网络的第一层 `tf.keras.layers.Flatten` 将图像格式从二维数组（28 x 28 像素）转换成一维数组（28 x 28 = 784 像素）。将该层视为图像中未堆叠的像素行并将其排列起来。该层没有要学习的参数，它只会重新格式化数据。\n",
    "\n",
    "展平像素后，网络会包括两个 `tf.keras.layers.Dense` 层的序列。它们是密集连接或全连接神经层。第一个 `Dense` 层有 128 个节点（或神经元）。第二个（也是最后一个）层会返回一个长度为 10 的 logits 数组。每个节点都包含一个得分，用来表示当前图像属于 10 个类中的哪一类。\n",
    "\n",
    "### 编译模型\n",
    "\n",
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的<em>编译</em>步骤中添加的：\n",
    "\n",
    "- <em>损失函数</em> - 测量模型在训练期间的准确程度。你希望最小化此函数，以便将模型“引导”到正确的方向上。\n",
    "- <em>优化器</em> - 决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- <em>指标</em> - 用于监控训练和测试步骤。以下示例使用了*准确率*，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:28.870668Z",
     "iopub.status.busy": "2022-08-31T04:53:28.870124Z",
     "iopub.status.idle": "2022-08-31T04:53:28.881808Z",
     "shell.execute_reply": "2022-08-31T04:53:28.881210Z"
    },
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## 训练模型\n",
    "\n",
    "训练神经网络模型需要执行以下步骤：\n",
    "\n",
    "1. 将训练数据馈送给模型。在本例中，训练数据位于 `train_images` 和 `train_labels` 数组中。\n",
    "2. 模型学习将图像和标签关联起来。\n",
    "3. 要求模型对测试集（在本例中为 `test_images` 数组）进行预测。\n",
    "4. 验证预测是否与 `test_labels` 数组中的标签相匹配。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### 向模型馈送数据\n",
    "\n",
    "要开始训练，请调用 <code>model.fit</code> 方法，这样命名是因为该方法会将模型与训练数据进行“拟合”："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:53:28.886019Z",
     "iopub.status.busy": "2022-08-31T04:53:28.885417Z",
     "iopub.status.idle": "2022-08-31T04:54:04.048100Z",
     "shell.execute_reply": "2022-08-31T04:54:04.047331Z"
    },
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5024 - accuracy: 0.8230\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3748 - accuracy: 0.8642\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3395 - accuracy: 0.8757\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3145 - accuracy: 0.8861\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2939 - accuracy: 0.8925\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2800 - accuracy: 0.8956\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2665 - accuracy: 0.9018\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2568 - accuracy: 0.9046\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2479 - accuracy: 0.9075\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2385 - accuracy: 0.9116\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e6a8809630>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "在模型训练期间，会显示损失和准确率指标。此模型在训练数据上的准确率达到了 0.91（或 91%）左右。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### 评估准确率\n",
    "\n",
    "接下来，比较模型在测试数据集上的表现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:04.052321Z",
     "iopub.status.busy": "2022-08-31T04:54:04.051605Z",
     "iopub.status.idle": "2022-08-31T04:54:04.911789Z",
     "shell.execute_reply": "2022-08-31T04:54:04.911006Z"
    },
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.3335 - accuracy: 0.8805 - 505ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.8805000185966492\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "结果表明，模型在测试数据集上的准确率略低于训练数据集。训练准确率和测试准确率之间的差距代表*过拟合*。过拟合是指机器学习模型在新的、以前未曾见过的输入上的表现不如在训练数据上的表现。过拟合的模型会“记住”训练数据集中的噪声和细节，从而对模型在新数据上的表现产生负面影响。有关更多信息，请参阅以下内容：\n",
    "\n",
    "- [演示过拟合](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "- [防止过拟合的策略](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### 进行预测\n",
    "\n",
    "模型经过训练后，您可以使用它对一些图像进行预测。附加一个 Softmax 层，将模型的线性输出 [logits](https://developers.google.com/machine-learning/glossary#logits) 转换成更容易理解的概率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:04.916151Z",
     "iopub.status.busy": "2022-08-31T04:54:04.915498Z",
     "iopub.status.idle": "2022-08-31T04:54:04.935475Z",
     "shell.execute_reply": "2022-08-31T04:54:04.934481Z"
    },
    "id": "DnfNA0CrQLSD"
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:04.938525Z",
     "iopub.status.busy": "2022-08-31T04:54:04.938306Z",
     "iopub.status.idle": "2022-08-31T04:54:05.583291Z",
     "shell.execute_reply": "2022-08-31T04:54:05.582541Z"
    },
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "在上例中，模型预测了测试集中每个图像的标签。我们来看看第一个预测结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.587967Z",
     "iopub.status.busy": "2022-08-31T04:54:05.587247Z",
     "iopub.status.idle": "2022-08-31T04:54:05.592407Z",
     "shell.execute_reply": "2022-08-31T04:54:05.591789Z"
    },
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.1722285e-06, 2.7008383e-11, 3.3052538e-09, 3.9512144e-11,\n",
       "       2.2820808e-08, 3.9780259e-02, 5.7088628e-06, 1.0073369e-02,\n",
       "       5.7337047e-08, 9.5013434e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "预测结果是一个包含 10 个数字的数组。它们代表模型对 10 种不同服装中每种服装的“置信度”。您可以看到哪个标签的置信度值最大："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.596022Z",
     "iopub.status.busy": "2022-08-31T04:54:05.595503Z",
     "iopub.status.idle": "2022-08-31T04:54:05.599901Z",
     "shell.execute_reply": "2022-08-31T04:54:05.599269Z"
    },
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "因此，该模型非常确信这个图像是短靴，或 `class_names[9]`。通过检查测试标签发现这个分类是正确的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.603514Z",
     "iopub.status.busy": "2022-08-31T04:54:05.602942Z",
     "iopub.status.idle": "2022-08-31T04:54:05.607157Z",
     "shell.execute_reply": "2022-08-31T04:54:05.606563Z"
    },
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygh2yYC972ne"
   },
   "source": [
    "您可以将其绘制成图表，看看模型对于全部 10 个类的预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.610554Z",
     "iopub.status.busy": "2022-08-31T04:54:05.610037Z",
     "iopub.status.idle": "2022-08-31T04:54:05.615905Z",
     "shell.execute_reply": "2022-08-31T04:54:05.615327Z"
    },
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')\n",
    "\n",
    "def plot_value_array_withLabel(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10), class_names, rotation=90)\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### 验证预测结果\n",
    "\n",
    "在模型经过训练后，您可以使用它对一些图像进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "我们来看看第 0 个图像、预测结果和预测数组。正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.619484Z",
     "iopub.status.busy": "2022-08-31T04:54:05.619035Z",
     "iopub.status.idle": "2022-08-31T04:54:05.723412Z",
     "shell.execute_reply": "2022-08-31T04:54:05.722677Z"
    },
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.727181Z",
     "iopub.status.busy": "2022-08-31T04:54:05.726487Z",
     "iopub.status.idle": "2022-08-31T04:54:05.829513Z",
     "shell.execute_reply": "2022-08-31T04:54:05.828871Z"
    },
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "让我们用模型的预测绘制几张图像。请注意，即使置信度很高，模型也可能出错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:05.833362Z",
     "iopub.status.busy": "2022-08-31T04:54:05.832741Z",
     "iopub.status.idle": "2022-08-31T04:54:07.426176Z",
     "shell.execute_reply": "2022-08-31T04:54:07.425483Z"
    },
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m0Th36tSJTSYTBwYG8saNG12ef8899/C0adNUjyMtLY2HDBnCn+qY3xESEsLHjx9nZubIyEjesGEDMzMfO3aM/f39dT2GkJAQ9vHxYQ8PD/bz8+PQ0FDVP1fq16/P69evVz0GZjGEMiQkRFcdmJm//vpr7tixo3Ldjh078jfSp9XMzEy7Qz59fX05MTGxwO2HDx9mPz8/3dc3oqtXmbt3Z166VLT9x4+LKV3Vq6vbiZLq+HnzTeY6dZgPH2a+ckV8wbDnr7/EtIE332Q+elTEsYAA5q+/Vt/vxg1x+/z5zh/nq6/aOmSYmZcvF/VISBDtq6OpXMziOfL2ZpY/nyxYwPzUU6INPnaM+e+/RQz18GDetMnxc1DUjp+GDUUsu3DBFuut14iMdFz3igCxQkCssEGsEPR0/DCLNicsTKzZhu8VavheIc7H9wrECivEitKLFeTry9ypk/iQLFu1SvRO+/uLX1c//9zWQN+6JRqI8HDRyx8WJtZGkB/XX3+J3mWzWTT2LVqID+BWehtoa5DR/hs92naf/HzmqVPFFw1fX/GrgPa5vHZNNMhms1hXYswY0fhrLVjAPGyY+rZLl0SZQUFijm9GhuP65uSI52PtWvv5ehpoZvHLbNOmotGtU8f2q4nVuXNiQdHAQBFsfv5Z/SXq88+ZW7US+RaLqL/1l2pm5tRUMUc3LExcIzyceeRIsWAps+MGOitLXOd2h3WZtXTpUvby8uLhw4fz3Llzee7cuTx8+HD29vZWvSGd6dKlC8+7PfHabDYrjdTEiRO5b9++usooid79WrVq8c7bkXPVqlUcFhbGiYmJ/Prrr/N9993n8nyz2czHbv+cHxISwn///Tczi0BTt25dl+d37tyZV91+0T7yyCPcr18/3rp1K48aNYrvln8ucuKrr75y+s8VPz8/Pnl7AQH5eTxw4AAHBgbqqkNxtG/fnp999tkCt0+cOJE7dOjg9uuXZTdvMk+ZwtymjWgbAgLEejmvvy4+zFuVVMfP5cu22EIkznPkhx+YmzUTcaFxY9Euan32mYhzKSmOy9m/X/1rLbP4sPv006J9vece0bnkzPDh4nmy2rOH+bHHxJcJX18xOur++8WHfKuS7PhZs0Y8Bi8vEXes+vQR62lUVIgVNogVxWe0WKG34ycrS3ymrFJFtGf4XmGD7xUiH98rECusECtKL1Y4GWQJRfXxx6LxNKJPPy04XLQsaty4Mc+ePbvA7R988AE3btxYVxlxcXFsNpv5qaeeYj8/P548eTL37t2bAwMDeZc8vteJkujd9/X1VVZ7Hz9+PE++/a3u+PHjuhr56tWr88HbK+Y1adJEGVq4d+9eXY3b2rVrecWKFcwsAstdd93FJpOJq1SpouwE4G5t2rThpUuXMrP6eYyOjubO2vk8brBp0yYODAzkJk2a8NixY3ns2LHcpEkTNpvNhfq1ByquhATxq20pb0Kh8vffok7OOr2MDrHCBrGi+BArwB3wvaL0IVbYIFYUX2nFCi/3rR5UcT35JFFKClFaGlFQUGnXpmR5exN99FFp18K148eP0+DBgwvcPmTIEHr11Vd1ldG5c2fau3cvzZo1i5o3b06//vortWnThrZv307NmzfXVUa7du3op59+omeffZaIiEy3V6r94osv6N5779VVRvXq1engwYNUs2ZNWrt2Lc2fP5+IiDIzM8nT09Pl+R07dqStW7dSkyZNaMCAAfSf//yH9u/fTytXrqSOHTu6PL9v375KukGDBnT48GFKTk6m0NBQ5fHYk5qaShaLRUk7Y72fI9OmTaPRo0fTuXPnKD8/n1auXEmJiYm0ZMkS+vHHH10+BiKxkNuHH35I3333HZ0+fZqys7NV+cnW1QXt6Nq1Kx05coQ++eQTOnz4MBERDR06lJ555hkKCwvTdX2o2Fq0IHrnHbE4ps7mw+0uXCBasoQoOLi0a1J6ECtsECsExAooa/C9ovQhVtggVgjlMla4rUsJoBTVr1+fFyxYUOD2+fPncwNH+0m6QUn07k+fPp2Dg4O5cePGXKdOHb558yYzMy9cuFDXAmRJSUnKImrp6en85JNPcvPmzXno0KHKMEd38PDw4EuXLjEzq7ZplP+52vpStmXLFu7VqxdXrVqV/f39uVOnTrxO3k/UhalTp3LNmjX5/fffZz8/P545cyaPGzeOK1euzHPnzi3SYwSA8g2xwgaxQkCsAAAtxAobxAqhPMYKE7OrjWEByp/58+fTc889R2PHjqX77ruPiIi2bdtGX331Fc2dO5eefPJJl2Xs2bOHvL29lV741atX06JFi6hp06Y0Y8YM8vHx0VWXpKQkmjVrFiUkJFB6ejq1adOGXn75Zd29+0RE33//PZ05c4b++c9/Uu3atYmIaPHixRQSEkIPPPCA7nL0Gjp0qO77rly50u7tmzdvpk6dOpGXlxdt3rzZaRldu3Z1mn/27FnlcWvt2LFD1y8M9evXp3nz5tHAgQMpKCiI9u7dq9y2Y8cO+vbbb52en5KSQgsXLqRDhw4REdHdd99NY8eOpeCKPFwCoJxDrCgexIqCECsAjAexongQKwoqjViBjh8wrFWrVtEHH3ygvKGaNGlCL774ou4G7Z577qEpU6bQsGHD6Pjx49S0aVMaOnQo7dy5kwYOHEhz5sxxY+3tu3nzJvn5+RXp3N27d6sal9atWzu875gxY5Q0M9OqVasoODiY2rVrp5SVkpJCQ4cOpUWLFhWpPoXRtGlT2rp1K1WqVEl1+7Zt22jgwIGUkpLisozAwEA6dOgQ1alTh2rWrEk//fQTtWnTho4fP06tW7emGzduODx3165d1LdvX/L396f27dsTEdHOnTspKytLGaoLAOUTYoUaYgViBQAUhFihhlhRDmNFKY42AijTLBaLsmr9rFmzuM/tlfW2bt3KtWvX1lVGz549edGiRXzjxo0i1yM3N5djYmI4LCyMPT09lQXIXn/9dWU7RmcuXbrE3bt3Z5PJpGw3aDKZuEePHnz58mWX57/00kv8xBNPcK60l2hubi7/61//4hdeeEH347h+/TqvW7eOly5dyosXL1b9c2XMmDHctm1bTk1NVW7bvHkzWywWu4vt2dOoUSPesWMHMzN36tSJ3769ldGyZcu4atWqTs/t3LkzR0VFcU5OjnJbTk4Ojx49mrt06aLr+gBgTIgVAmIFYgUAOIZYISBWlF6sQMcPGFJERARfvXq1wO3Xr1/niIgIXWUEBQXxkSNHmJm5V69ePGfOHGZmPnXqFPv5+ekqY9KkSVyjRg329/fnf/zjHxwbG8vZ2dk6H4UQHR3NkZGR/PXXX6tW81+2bJmuubjDhw/ndu3aKSvwM4vtCtu1a8cPP/ywy/OrVKnChw8fLnD74cOHuVKlSroew5o1azgoKIhNJhMHBwdzSEiI8i80NNTl+Xl5efzQQw9x165d+ebNm7xhwwY2m83K30SPl19+md+8vffrsmXL2MvLixs0aMA+Pj788ssvOz3Xz8+PDx06VOD2AwcOsL+/v+46AEDZglhhg1ghIFYAgBZihQ1ihVAeYwU6fsCQTCaTsgCY7OLFi+zj46OrjO7du/OoUaN4yZIl7O3tzUePHmVmsQVf3bp1ddclLy+P161bx6NHj2aLxcKhoaE8fvx43rRpk67z69evz+vXr2dm9ZaDhw4d4pCQEJfnWywW/uuvvwrc/ueff3JwcLDL80NCQjg2NrbA7bGxsbquz8zcsGFDnjx5MmdkZOi6vz23bt3iXr168X333cdms5k/+uijIpfFzLx9+3b+4IMPeM2aNS7vW61aNbsLvq1du5arVatWrHoAQOlBrLBBrLAPsQIAECtsECvsKw+xAtu5g6GsWbNGSa9bt061QFZeXh79/vvvVK9ePV1lzZkzh0aOHEmxsbH02muvUYMGDYhILIhmXdhNDw8PD+rTpw/16dOHFixYQD/88AO9+eabtHDhQsrLy3N5/rlz55Rry/Lz8yknJ8fl+fn5+eTt7V3gdm9vb8rPz3d5/pgxY2jcuHGUlJSkzEP9888/adasWao5u86cO3eOJk2aRAEBAbruT0S0b9++ArfNmDGDHnnkEXrsscfo/vvvV+7TokULl+Vdu3aNKleuTEREZ86coZ9//pmysrKU+cXOjBgxgsaNG0fvv/++alG/F198kR555BHdjwkAygbEioIQKwTECgCwQqwoCLFCKJexwm1dSgClwGQyKVv5WdPWfz4+PtyoUSP+4YcfinWNrKysQg+rZGa+cOECf/jhh9y2bVs2mUzcoUMHXee1adOGly5dyszqnvno6Gju3Lmzy/OHDBnC999/P587d0657ezZs9y1a1d+8MEHXZ6fl5fH77zzDoeFhSnPZVhYGL/zzjuq+bnOPPTQQ7x8+XJd97Wy93eUj/Vu27hv3z6uW7cue3h48F133cXx8fFcvXp1NpvNbLFY2NPTk1etWuW0jFu3bvGkSZPYx8dH2TLS19eXn3vuOWUbTAAoPxArCkKsQKwAADXEioIQK8pvrMCuXmBIERERtHPnTqpSpUqp1iM1NZVWrFhB3377LW3atIkiIyNp5MiRNHLkSKpfv76uMlavXk2jR4+mV155hWJiYig6OpoSExNpyZIl9OOPP1Lv3r2dnn/mzBkaMmQIHThwgMLDw5XbmjVrRmvWrHG4naGjx0NEZLFYXN5X/pXkypUrFBMTQ2PGjKHmzZsX+KVgyJAhBc4/deqU7nrVrVvXYV7//v3Jy8uLpkyZQkuXLqUff/yR+vbtS//3f/9HRETPPvss7d69m3bs2OHyOpmZmZSUlEREYhvHwvzSAABlD2KFDWIFYgUA2IdYYYNYUX5jBTp+wFBGjRpFDzzwAPXt25fMZnOxyvLw8CCTyeQwX89wSn9/fwoNDaURI0bQyJEjdQ3/sycuLo5iYmIoISGB0tPTqU2bNjRt2jTq06ePrvOZmdavX0+HDx8mIrEFZa9evYpUF708PDx03c9kMul6LouqSpUqtGHDBmrRogWlp6eTxWKhnTt3Utu2bYmI6PDhw9SxY0ddWzcSieBGREqwA4DyB7HCPsQKxAoAsEGssA+xonzGCqzxA4bSoEEDeuutt+ixxx6jbt260ZAhQ2jIkCFUq1atQpe1atUq1XFOTg7Fx8fT4sWLKTo6WlcZa9asoZ49e+purLRyc3PprbfeorFjx9Jvv/1WqHM3bNhAEydOpB07dpDFYqHevXsrvfg3btygu+++mxYsWEBdunRxWs6lS5fohRdeoN9//50uX75M2r5iR42rnnm+hXXw4EE6ffo0ZWdnq26317NvlZycTDVq1CAiIrPZTIGBgRQaGqrkh4aGUlpamtPr5ubmUnR0NM2bN4/S09OVsp599lmaPn263bnOAFB2IVbYIFYIiBUAoIVYYYNYIZTrWOG2SWQApej06dP8ySefcJ8+fdjX15fbtGnD0dHRHB8fX+yyv/nmGx4yZEihzrl8+TLHxcVxXFwcX758uVDnBgYG8okTJwp1DjPz4MGDefbs2Q7z586dq2subr9+/bhp06b86aef8qpVqzg2Nlb1z5k//vijwNznxYsXc7169bhq1ao8fvx4XXNZk5KSuEWLFnbn47qai2symVTPudls5uPHjyvHFy9edFnGU089xdWqVeMFCxZwQkICJyQk8IIFC7hGjRr81FNPuaw/AJRNiBWIFVaIFQDgCGIFYoVVeY4V6PgBw0tNTeXly5fzo48+yqGhoVynTh2eMGEC//3330UqLykpiQMDA3XdNyMjg8eMGcOenp5Ko+Ll5cVjx47VvQXhkCFD+Kuvvip0PevUqcMHDx50mH/o0CEODw93WY7ZbC5yYOvbty/PmjVLOd63bx97eXnxE088wR988AHXqFGDp0+f7rKcQYMG8QMPPMBXrlxhs9nMBw8e5Li4OG7fvj1v2bLF6bkmk4kHDBjADz30ED/00EPs5eXFffr0UY4HDBjgsoG2WCz8888/F7j9p59+YovF4rL+AFD2IVbYh1iBWAEANogV9iFWlP1YgaleYHhBQUE0fPhwGj58OOXl5dHmzZtp9erVtH37drr77rsLVVZWVhbNmzdP9xDPf//737R582b64YcfqFOnTkREtHXrVpo0aRL95z//ofnz57sso3///jRlyhTav38/tW3blgIDA1X5joYjXrp0yelQQS8vL7py5YrL64eHhxcYhqlXQkICvfHGG8rxsmXLqEOHDsoCaOHh4TR9+nSaMWOG03K2b99OGzZsoCpVqpCHhwd5eHhQ586d6e2336ZJkyZRfHy8w3NHjx6tOn7ssccK3GfUqFFOr+/r62t3u86IiAjy8fFxei4AlA+IFfYhVtggVgAAYoV9iBU2ZTZWuK1LCaAUde/ena9fv17g9hs3bnD37t11lRESEsKhoaHKv5CQEPb09OSgoCBevXq1rjIqV67MGzduLHD7hg0buEqVKrrK0G4fqd2G0JHIyEin2wmuWLGCIyIiXF5/3bp13KdPnyINC/X19eXTp08rx506deI33nhDOT5x4gSbzWaX5YSEhCjDKCMjI3nDhg3MzHzs2DH29/cvdL0KKzo6mh955BHV8NGbN2/yyJEjecaMGW6/PgC4B2IFYkVJQqwAMCbECsSKklRasQIjfsCQNm3aVGChLiKimzdvUlxcnK4y5syZozr28PCgqlWrUocOHVSLeDmTmZlJ1atXL3B7tWrVKDMzU1cZRV3MbMCAATR16lTq168f+fn5qfKysrJo+vTpNGjQIJfljBgxgjIzM5VtBrW9/cnJyQ7PrV69Op04cYLCw8MpOzub9uzZo1rALi0tTdcCZs2aNaOEhASKiIigDh060Lvvvks+Pj70+eefU2RkpMvzi2Lo0KGq4/Xr11Pt2rWpZcuWRCR+dcjOzqaePXu65foA4H6IFYgVxYVYAWB8iBWIFcVVFmIFOn7AUPbt26ekDx48SBcvXlSO8/LyaO3atbqHU2qH8hXFvffeS9OnT6clS5YojWRWVhZFR0fTvffe6/L8/Px8+uqrr2jlypV08uRJMplMFBkZScOGDaPHH3/c6baQr7/+Oq1cuZIaNWpEEydOpLvuuouIxDaDn3zyCeXl5dFrr73msg7aQFUYAwYMoClTptA777xDsbGxFBAQoFrtf9++fVS/fn2X5bz++uuUkZFBREQxMTE0aNAg6tKlC1WuXJmWL19e5Po5ExwcrDoeNmyY6hhb9AKUX4gVNogVxYNYAWBciBU2iBXFUyZihdvGEgGUAnlFdntDGAMCAnjhwoW6y7t+/Tq///77PG7cOB43bhzPnj2bU1JSdJ+/f/9+DgsL48qVK3OPHj24R48eXLlyZa5Vq5bLReDy8/N54MCBbDKZuFWrVvzwww/ziBEjlFXoH3jgAZfXP3nyJPfv37/AivX9+/dXrUDvLleuXOEuXbqwyWTioKAgXrlypSq/R48e/Oqrrxap7GvXrnF+fn5JVBMAKhjECjXECgCAghAr1BAryjcTcxFXVwIog06dOkXMTJGRkfTXX39R1apVlTwfHx+qVq0aeXp66ipr165d1LdvX/L396f27dsTEdHOnTspKyuLfv31V2rTpo2ucjIzM+mbb76hw4cPExFRkyZNaOTIkeTv7+/0vEWLFtHkyZNp9erV1L17d1Xehg0b6MEHH6SPP/7Y5QJiRETXr1+nY8eOETNTw4YNdQ8p1bp582aBoa4Wi8XleTdu3CCz2VzguU9OTiaz2ax7IbNjx45RUlIS3X///eTv70/M7PTXCQAAexAr7EOsAACwQaywD7GinCqlDicAt8nOzuaoqKhi9zx37tyZo6KiOCcnR7ktJyeHR48ezV26dCluNV3q3bs3v/322w7z33zzTe7Tp4/b65Gens4TJkzgqlWrKr96yP/uhKtXr3KPHj2UXxaSkpKYmXnMmDH8/PPP35E6AICxIFaULMQKADAixIqShVhRejDiBwwpJCSE4uPjKSIioshl+Pv7U3x8PDVu3Fh1+8GDB6ldu3YOF1Fbs2aN7ms42jKRiKhGjRq0du1aatWqld38+Ph46t+/v2q+sTtMmDCBNm7cSDNnzqTHH3+cPvnkEzp37hx99tlnNGvWLBo5cqRbr08ktkW8fPkyffHFF9SkSRNKSEigyMhIWrduHT3//PN04MABt9cBAIwHsaLkIFYAgFEhVpQcxIpSVNo9TwDuMGrUKJ49e3axyqhWrRqvW7euwO1r167latWqOTzP2TaJerdMZGb29vbm8+fPO8w/d+4c+/j46H9ARRQeHq5sHRkUFMRHjx5lZuYlS5Zw//793X59Zubq1avz3r17mZnZbDYrPfNJSUkcGBh4R+oAAMaDWFFyECsAwKgQK0oOYkXpwa5eYEgNGzakmJgY2rZtG7Vt25YCAwNV+ZMmTXJZxogRI2jcuHH0/vvv03333UdERNu2baMXX3yRHnnkEYfnFXWbRK28vDzy8nL8FvX09KTc3NwSuZYzycnJytaGFotF2Waxc+fO9PTTT7v9+kREGRkZFBAQYLduvr6+d6QOVjdv3iywjSUAlE+IFSUHsUINsQLAOBArSg5ihdqdjBXo+AFDWrhwIYWEhNDu3btp9+7dqjyTyaSrgX7//ffJZDLRqFGjlIbQ29ubnn76aZo1a5Zb6i1jZoqKinLYAN26dcvtdSAiioyMpBMnTlCdOnWocePG9N1331H79u3phx9+oJCQkDtShy5dutCSJUto5syZRCT+hvn5+fTuu+8WWKDOHfLz8+nNN9+kBQsW0KVLl+j/2bvv8Ciqtg3g9yaQXulBQAhFQDqIVOlIUUR8xYLSVVReUFREEYFgQVGkqOD7CYJYQKUKikov0qT3EkB6DSGFlvJ8fxwyOzPZ3Wwam0zu33VxcXbOzszZze55Zs6ecujQIURGRmLEiBEoX748+vXrl+tlIKKcx1iRcxgrGCuIrIqxIucwVnguVnCOHyIHUlJSsH79etSsWRO+vr6Ijo4GAFSsWNFhC7HepEmT3D6Pq0DRp08ft47xzTffuH2+rPjss8/g7e2NQYMGYdmyZXj44YchIkhKSsL48eMxePDgXD0/AOzZswdt2rRBvXr1sGLFCnTp0gV79+5FTEwM1q9fj4oVK+bq+aOiojBz5kxERUXhueeew549exAZGYk5c+ZgwoQJ2LBhQ66en4jyJsYKO8YKxgoicoyxwo6xwnOxgg0/RE74+flh//79mZ7Izd3n22w2HD16NCtF86h///0XW7duRaVKlVCrVq07dt6rV6/i888/x86dO5GQkIB69erh5ZdfRkRERK6fu1KlSvjqq6/Qpk0bBAcHa5PAHThwAI0bN8aVK1dyvQxElDcxVjjGWMFYQUR2jBWOMVbcuVjBoV5kGUOGDMGYMWMQGBiIIUOGuHzu+PHjMzxejRo1cPTo0UxX0MeOHcvU8/OqDRs24PLly3jooYe0bd9++y1GjhyJxMREdO3aFZMnT75jY2FDQ0MxfPjwO3Ius9OnT6NSpUrptqempiIpKckDJSKirGKsyFmMFXaMFUTWwViRsxgr7DwVK9jwQ5axfft27cuyfft2p8+z2WxuHe+9997D66+/jjFjxjicyC0kJCTrhc0HoqKi0LJlS62C3r17N/r164fevXujevXq+Pjjj1G6dGmMGjXqjpQnNjYWmzdvxoULF9JNdNezZ89cPXf16tWxdu1a3H333Ybtv/zyC+rWrZur5yainMVYkbMYK+wYK4isg7EiZzFW2HkqVnCoF5FJVFQUXnvtNQQHB2vb9JW6iMBmsyElJSXDY/Xt29dl/vTp07Ne0FwWERGBX3/9FQ0aNAAADB8+HKtXr8a6desAAD///DNGjhyJffv25XpZfv31V/To0QMJCQkICQkx/D1sNpu2IkBuWbhwIXr16oW33noLUVFRGD16NA4ePIhvv/0WixcvRrt27XL1/ESU9zBWKIwVdowVRGTGWKEwVth5Klaw4YfIxNvbG2fPnsX+/ftdPq9FixYZHuvRRx81PE5KSsKePXsQGxuL1q1bY968edkqa27y8/PD4cOHUbZsWQBqmcWOHTtq3SKPHz+OmjVrIj4+PtfLUqVKFXTq1AkffPBBhpPg5Za1a9ciKirKMBb43XffRfv27T1SHiLyLMYKhbHCiLGCiPQYKxTGCiNPxAoO9SJLSkxMxNixY7F8+XKHXfhcTX6W1hbqTgWckfnz56fblpqaihdffDHXZ4zPrpIlS+LYsWMoW7Ysbt26hW3btmH06NFafnx8PAoXLnxHynL69GkMGjTIY5UzoJZ+/Ouvvzx2fiLKeYwV2cdYYcRYQWQ9jBXZx1hh5IlYwYYfsqT+/ftj9erVePbZZxEREeH2+Ns0mX1+Znh5eWHIkCFo2bIlhg4dmmvnya5OnTph2LBh+Oijj7BgwQIEBASgefPmWv6uXbvuWJB58MEH8c8//yAyMvKOnI+ICgbGiuxjrCAiq2OsyD7GCs9jww9Z0u+//44lS5agadOmWdq/SpUqGVbS2Rn/GR0djeTk5CzvfyeMGTMG3bp1Q4sWLRAUFISZM2fCx8dHy58+ffod67reuXNnvPHGG9i3bx9q1qyZ7heBLl265Pg5w8PD3Q7UuT0WmIhyB2NF9jFWMFYQWR1jRfYxVng+VrDhhywpPDwcRYoUyfL+o0ePRmhoaLbLYV7+UURw9uxZLFmyBL169cr28XNTsWLFsGbNGly9ehVBQUHw9vY25P/8888ICgq6I2V57rnnAKgJ8szcnRAvsyZMmJDjxySivIWxIvsYKybk+DGJKG9hrMg+xooJOX7MzOLkzmRJ3333HRYuXIiZM2dmevyml5cXzp07hxIlSmS7HK1atYLNZtPG93p5eaF48eJo3bo1+vbti0KF2Paa312/fh3+/v6eLgYRZQFjBd0pjBVE+RdjBd0puRkr+Okgy6hbt66hC92RI0dQsmRJlC9fPl0Xvm3btjk9Tk6Mw01NTcW4ceNw8+ZNJCUloXXr1hg1ahQv+jJpw4YNuHz5Mh566CFt27fffouRI0ciMTERXbt2xeTJk+Hr65ur5Rg0aBAmTZqUbntiYiIeeughrFy5MlfPT0Q5h7HCehgriCinMVZYT0GPFWz4Icvo2rVrjhwnJzrBvf/++xg1ahTatm0Lf39/TJo0CRcvXsT06dNzoIQFR1RUFFq2bKlV0Lt370a/fv3Qu3dvVKtWDePGjUPp0qUxatSoXC3HkiVLEB4eblh9IDExER06dMjV8xJRzmOssB7GCiLKaYwV1lPgY4UQUY6rVKmSTJ06VXv8119/iY+Pj6SkpHiwVPlPqVKlZMuWLdrjt99+W5o2bao9/umnn6RatWq5Xo4jR45IRESEfPbZZyIiEhcXJ40bN5bmzZtLQkJCrp+fiKyJsSJnMFYQkZUxVuSMgh4r2OOHLOnkyZOw2WwoU6YMAGDz5s344YcfUL16dTz//PO5fv4TJ06gU6dO2uO2bdvCZrPhzJkzWpkoY1euXEHJkiW1x6tXr0bHjh21x/fddx9OnjyZ6+WoWLEili5dilatWsHLyws//vgjfH19sWTJEgQGBub6+YkodzBWWANjBRHlJsYKayjoscIr145M5EFPP/20Nj7y3LlzaNu2LTZv3ozhw4c7nME9pyUnJ8PPz8+wrXDhwkhKSsr1c1tJyZIlcezYMQDArVu3sG3bNjRq1EjLj4+PTzfOOrfUqlULixcvxttvv42AgAD8/vvvvJAnyucYK6yBsYKIchNjhTUU9FjBHj9kSXv27EHDhg0BAD/99BNq1qyJ9evX488//8SAAQPw7rvv5ur5RQS9e/c2TA5248YNDBgwwPClnjdvXq6WI7/r1KkThg0bho8++ggLFixAQEAAmjdvruXv2rULFStWzJVzmyf1S+Pr64szZ86gadOm2jZXk/oRUd7FWGENjBVElJsYK6yhoMcKNvyQJSUlJWmV47Jly9ClSxcAQNWqVXH27NlcP3+vXr3SbXvmmWdy/bxWM2bMGHTr1g0tWrRAUFAQZs6cCR8fHy1/+vTpaN++fa6cO6cm9SOivIuxwhoYK4goNzFWWENBjxU2kRyYapwoj7n//vvRqlUrdO7cGe3bt8fGjRtRu3ZtbNy4Ef/5z39w6tQpTxeRMuHq1asICgqCt7e3YXtMTAyCgoIMlTYRkbsYK6yFsYKIcgNjhbUU1FjBhh+ypFWrVuHRRx9FXFwcevXqpS13+Pbbb+PAgQPsCklZcuvWLVy4cAGpqamG7eXKlfNQiYgoOxgrKDcwVhBZC2MF5YY7HSvY8EOWlZKSgri4OISHh2vbjh8/joCAAJQoUcKDJaP85tChQ+jXrx/+/vtvw3YRgc1mQ0pKiodKRkTZxVhBOYWxgsi6GCsop3gqVnCOH7Isb29vhIeHY+zYsRgwYADCwsJQvnx5TxeL8qE+ffqgUKFCWLx4MSIiIhxOzkZE+RNjBeUUxgoi62KsoJziqVjBHj9keSEhIdixYwciIyM9XRTKpwIDA7F161ZUrVrV00UholzCWEHZxVhBZH2MFZRdnooVXnf0bEQewLZNyq7q1avj0qVLni4GEeUixgrKLsYKIutjrKDs8lSsYMMPWUpUVBSuXbvm6WKQxXz00UcYOnQoVq1ahcuXLyMuLs7wj4jyF8YKyg2MFUTWwlhBucFTsYJDvchSvL29cfbsWcMkaydPnkTp0qXTLdlH5C4vL9VGbh6Dywk7ifInxgrKDYwVRNbCWEG5wVOxgpM7k6U4ascsW7asB0pCVrJy5UpPF4GIchBjBeUGxgoia2GsoNzgqVjBhh+yHK6iQTmtRYsWTvP27NlzB0tCRDmFsYJyGmMFkfUwVlBO81Ss4FAvshQvLy+EhoZmWEnHxMTcoRKRFcXHx+PHH3/E119/ja1bt7L7PlE+w1hBdwJjBVH+xlhBd8KdihXs8UOWM3r0aISGhnq6GGRBa9aswbRp0zB37lyULl0a3bp1wxdffOHpYhFRFjBWUG5hrCCyDsYKyi13Olaw4Ycs58knnzRMwkaUHefOncOMGTMwbdo0xMXFoXv37rh58yYWLFiA6tWre7p4RJRFjBWUkxgriKyJsYJykidjBZdzJ0vhOFzKSQ8//DDuuece7Nq1CxMmTMCZM2cwefJkTxeLiLKJsYJyEmMFkTUxVlBO8nSsYI8fshROWUU56ffff8egQYPw4osvonLlyp4uDhHlEMYKykmMFUTWxFhBOcnTsYI9fshSUlNT2R2Tcsy6desQHx+P+vXr4/7778fnn3+OS5cuebpYRJRNjBWUkxgriKyJsYJykqdjBVf1IiLKQGJiIubMmYPp06dj8+bNSElJwfjx49G3b18EBwd7unhERJQHMFYQEVFGPBUr2PBDRJQJBw8exLRp0zBr1izExsaiXbt2WLRokaeLRUREeQhjBRERZeROxgo2/BARZUFKSgp+/fVXTJ8+nRfzRETkEGMFERFl5E7ECjb8EBERERERERFZFCd3JiIiIiIiIiKyKDb8EBERERERERFZFBt+iIiIiIiIiIgsig0/REREREREREQWxYYfIiIiIiIiIiKLYsMPEREREREREZFFseGHiIiIiIiIiMii2PBDRERERERERGRRbPghIiIiIiIiIrIoNvwQEREREREREVkUG36IiIiIiIiIiCyqUFZ3TE1NxZkzZxAcHAybzZaTZaJMEBHEx8ejdOnS8PKydjseP3NkBQXpOwvwe5tXFKTPHT9zZAUF6TsL8HubVxSkzx0/c2QFmfnOZrnh58yZMyhbtmxWd6ccdvLkSZQpU8bTxchV/MyRlRSE7yzA721eUxA+d/zMkZUUhO8swO9tXlMQPnf8zJGVuPOdzXLDT3BwsHaSkJCQrB4mU0RES7tqmb1y5YrhcXh4uJY+evSoIS8mJkZLm1vJfH19tfS9996bucLeIXFxcShbtqz297AyT3zm8qOTJ4H69YGbNzO/r68vsHUrwDiYewrSdxbwzPc2NTVVS+vjBgB4e3s73e/HH3/U0ps3bzbkJScna+mwsDBDXpUqVbT0s88+6/T45rK4+wtjVvfTK0ifO8aK/GPHDqBFi6zvv3o1UKdOTpUmbylI31mA39u8oiB97viZu3OyU9dbuZ7PCZn5zma54SftwjMkJCTPNfykpKQYHuvLZ35Tburujs03BPqGn7xeIRSELoqe+MzlRzdvZq3RR78v397cVxC+s4BnvrdZbfjx9/fX0j4+PoY8/Q8D+thg3s/Va/Rkw09O7JtfMFbkH0FB2d/f6n/igvCdBfi9zWsKwueOn7k7Jzt1fUGo53OCO9/ZLDf83AnmBhz9Bbv5Qlh/IZ6UlGTI01+UX79+3ZCn/+XWvF/hwoW19HPPPWfI+/jjj10VnYiIPMTdeQl27dpleNyrVy8t3bhxY6fH1McGAPjss88cHgMwxi1zUHb3x4yCcAFORERERLnH2rN2EREREREREREVYGz4ISIiIiIiIiKyKDb8EBERERERERFZVJ6e48fVJJxz5swxPH733Xe1tHnehp9//llLv/HGG4a87du3a+lly5YZ8tq2baulX3rpJUOefoWXQoWMb6O78zYQEVHuOnDggOHx+fPntXSJEiUMeZs2bdLSI0eONORdvXpVS+vnjQOAr7/+WkuvWbPGkLdu3Tot/eabbxryzBNIExERERHlBvb4ISIiIiIiIiKyKDb8EBERERERERFZVJ4e6uWKeXhV6dKltfQ777xjyOvUqZOWXrp0qSHv2LFjTs/x5Zdfauny5cu7XTYO7yIiunO2bt1qeLxgwQItfebMGUNe06ZNtXRsbKwhr0iRIlr6nnvuMeRduHBBS5uHetWuXVtL37p1y5AXEhKipT/++GNDXosWLbR0tWrVDHnFihUDEREREVFOYI8fIiIiIiIiIiKLYsMPEREREREREZFFseGHiIiIiIiIiMiiPDLHj6vlzvXzI2zbts2Qp5+P4caNG4a8I0eOaOk9e/YY8n777TctHRYWZsiLiIjQ0ocOHXJa5oMHDxoe37x5U0vr5xcCgKSkJC1dsmRJQ56XF9vaiIiyS780eps2bQx5+vlxzHP11KhRQ0sfP37ckDdr1iwtXb9+fUNelSpVtLS+/geARYsWaekHH3zQkKefu2fjxo2GvGXLljnN69q1q5auXLkyiIiIiIiyiq0QREREREREREQWxYYfIiIiIiIiIiKL8shQL1fLne/bt09Lb9myxZCn77Kv73YPAHXq1NHSp0+fNuQlJCRoaf0yvwBQt25dLX3p0iVD3vXr17V0YGCgIe/y5cta+vDhw4Y8Hx8fLV24cGFDHpfoJSLKvN27dxse64dXffTRR4a88uXLa+lChYxhLjIy0uHzAODKlStauk+fPoa8o0ePaulr164Z8nbs2KGl77//fkOe/rnmYcF33XWXw2MAwPjx47X0lClTQERERESUVezxQ0RERERERERkUWz4ISIiIiIiIiKyKDb8EBERERERERFZlEfm+HFFP8dCpUqVDHn6uXqKFy9uyIuLi9PSRYsWNeTp59X5559/DHmbN2/W0vplfgHg4sWLWjo+Pt6QFx4e7vR8+iXb9fMEERFR1mzdutXweOnSpVp6+vTphryFCxdqaXP9rF9e/cCBA4a8X3/9VUvrYwpgXPr9/Pnzhjz9PG8lS5Y05B08eFBL6+cJAoAiRYpo6erVqxvyOnfuDCIiIiKinMAeP0REREREREREFsWGHyIiIiIiIiIii/L4UC/98C3AOKQqIiLCkKdfvrdmzZqGvBs3bjg9R1BQkJa+deuWIU8/FMu89HpKSoqWNi9BHxAQ4DANGJfvNS/7S0REmbdixQrD4woVKmjpOnXqGPJCQ0O1tL7+B4xDev/9919Dnj7mtG7d2pAXHR2tpZOSkgx5+qXm9UOLAeOwMPMwMPNx9E6dOqWlL126ZMgzn4OIiIiIyBX2+CEiIiIiIiIisig2/BARERERERERWRQbfoiIiIiIiIiILMrjc/zExsYaHt+8eVNLlypVypCnnytBv9Q6AAQGBmppb29vQ56fn5+WDgkJMeTp5/UREUOefhlg/ZwRAJCamuowDRjnETLP4aB/fb6+viAiooyZl1c/efKklm7QoIEhTz9Xj3n+t7CwMC195coVQ54+HlSuXNmQd/XqVS1tntdNv2S7fp468/n0sQgAWrRooaXnzp1ryNMvEX/58mVDHuf4ISIiIqLMYI8fIiIiIiIiIiKLYsMPEREREREREZFF5bmhXj4+Plpa30UeAMLDw7W0fsiUOc88ZMvLy96+Ze727+/vr6XN3fD1z9Uv+w4Yu/rrh6ABQHJystPXoB+uULx4cRARUcbMdal+mNZvv/1myNPXrea6Wz+E+Pjx44Y8/WNz3oEDB7R0kSJFDHlHjx7V0v379zfknTlzRkvv2LHDkLd69Wot/ffffxvy9DHGHO+IiIiIiDKDPX6IiIiIiIiIiCyKDT9ERERERERERBbFhh8iIiIiIiIiIovy+Bw/5vkX9HP8mJdl1z/30qVLhjz9nA76OX0AwGazOT1/oUL2tyAlJcWQp1+m3bz0un4/89xA+jwz83OJiChj9evXNzzu1auXljbPj6OfcycmJsaQd/bsWS1tXs49ISFBS5vnn9Mv026OFfrl1k+dOmXI0y/Lfu3aNUOePo6Zl6TXz2lknlOIiIiIiCgz2OOHiIiIiIiIiMii2PBDRERERERERGRRHh/qZV5eXT+8yzwsSr9surmLvr5bvL7bPQDcunVLS5uHYenPpx9mBhiHjOmHfQFASEiIltYvyQsAdevW1dLmYWbmpeaJiMix3bt3a+kff/zRkPfUU09paXP9nJycrKVDQ0MNeUFBQU7z9LFCnwaApKQkp+UsWrSo02PqY455GLI+5nTo0MGQd+7cOS29cuVKQ96zzz7rtCxEROSY+f5APyzYPBT3xIkTWrpGjRqGvP/9739a2lwfly5dWkub40F4eLjTsunjmDlWuKK/r3A1tQUREXv8EBERERERERFZFBt+iIiIiIiIiIgsig0/REREREREREQW5fE5fszzKAQEBGhp83w4cXFxWrpUqVKGPP3Su+YxrvqxsuY5fvRjas37FS5cWEvr54ww++WXXwyPq1SpoqX1Y32B9HMaERGRY4mJiVpaP+cNAMyYMUNL//bbb4a8kSNHaml9fQwAJUuW1NLmeXtOnz6tpRs3bmzI08eREiVKGPL0y61XrlzZkKd/rnluukcffVRL79+/35C3c+dOLV2vXj1DHuf4ISKrEhGH82G6mr8mJSVFS+vn7gSAFStWaOnJkycb8qKjo7W0Pt4AxjnYKlasaMjTzznaokULQ97nn3+upZctW2bIW7RokZZu1KiRIc/VvD76eyXzfKSc14eI3MUeP0REREREREREFsWGHyIiIiIiIiIii/L4UK+bN28aHuuXOjR39Txw4ICWNi/17uvrq6WvX79uyNN3ATXT55m7WeqHgemXADabP3++4fFrr72mpc1dMhMSEpweh4iI7KpXr66lP/zwQ0Ne+/bttXTx4sUNeXPnztXS5uV0y5Qpo6XNdf4PP/ygpSMjIw15+iEBZ8+eNeStXbtWS5uX6z158qSWjo+PhzOdOnUyPG7VqpWW1r8PREQFgf4aHHA9FEo/vGvbtm2GvAkTJmjpe+65x5D3xBNPaOn69esb8sLCwrS0eTjxhg0btPT//d//GfKCg4O1tH5oMWAc3luhQgVD3rBhw7R0ly5dDHnmewkioqxgjx8iIiIiIiIiIotiww8RERERERERkUWx4YeIiIiIiIiIyKI8PsePeRnCkJAQLW2e/+f48eNaWj+G1vxc85Lp+mXZzWOE9Y/NS0Dq6ZeZB4zzD5mXltcvCVyrVi1DnnnMMhEROXb48GEtfejQIUOevr6+cOGCIS85OVlLm+d4088BZ67z9fPx7N2715Cnn2POHJv08cC8RPyJEye0dExMjCHv3nvv1dLmuSD0r33Xrl2GPHNcISKyCpvNBpvN5vKa3BXzXD2XL1/W0kWKFMnSMXv16uXysd6xY8e09HvvvWfI27Fjh5Y2z/mpn8dOfwwAiIiI0NLmOKKPP+Z4p7/nMM+bqo9VrVu3dlouIrIO9vghIiIiIiIiIrIoNvwQEREREREREVmUR4Z66Ydimbsl6pdlj4uLc3qMa9euGR4HBgZq6UKFjC9LP9TL3A1fTz88ADAOQzMvpagfznXmzBlD3qlTp5yeg0O9iIjcox/u5OfnZ8jT1+U//fSTIW/s2LFaWj+cCjAu0Wuuj/VDep9++mlD3vbt252WRd8tv2PHjoa8xo0ba2lzF/1XX33V4fEBY4zTxzAAiI2N1dL610NElN+dPn0acXFx6Yb36utnf39/Q55+2oZXXnnFkKcfmvv3338b8vR1qXmaCH2MMd+rbN68WUufO3fOkKefsqJq1aqGvHbt2mnpypUrG/LKlCmjpRcsWGDIW7t2rZY2T3Whjw/mmKa/rzHn6V/7fffdp6UTExNBRNbEHj9ERERERERERBbFhh8iIiIiIiIiIotiww8RERERERERkUV5ZI4f81hZPf3YWPP4Xj3z+F793EDm4+uXJjQvD6kf82pe6l2fZ57/56677tLSrpbhNdPPG2QuZ1aXriQisqKtW7dqafMyvPoleg8ePGjI08/ztmLFCkPePffco6XNy9auXr1aS9etW9eQp49H+rkRzGV54IEHDHkbNmzQ0ua54sqVK6elzXP86GPMpUuXDHkXL17U0pzjh4isJDAwEEFBQenm3Dlx4oSWNs+lqb9+r1mzpiFv2rRpTs+ln//HXD/r5w4tUaKEIa979+5aukKFCoY8/dLrWfXCCy8YHuvnPDXPY2q+P9HTL+FuXs5dTx9HXM2vSkT5G3v8EBERERERERFZFBt+iIiIiIiIiIgsyiNDvfTMw6v0yzVu27bN6X7moV7Xr1/X0uYhU/qlDl0NpzJ3n9SXzVUXyaCgIMNj87ADPVfDxzjUi4jITr8UeqNGjQx5e/bs0dLNmjUz5IWHh2vp3bt3G/Ju3bqlpc3L2+rrefNQXH1s0g+1Mj/XXK/rlwQ2DyXQxw5z93r9cIX4+HhDnnnYARGRVYSFhSEkJASdOnXydFHyDP00GEREWcUeP0REREREREREFsWGHyIiIiIiIiIii2LDDxERERERERGRRXlkjh/9nAfmORb0y53HxMQ4PUZwcLDhcWJiopbWz+EAGOdfMM+x4GoZRP2cO/o5hADjvEFFixY15Olfn5m78wYRERV0O3bs0NKVKlVymqdf+hwAzp49q6VPnz5tyNMvtWueO8fVcsHHjh1zmnft2jUtff78eafHNMeKKlWqaGlz3CpTpoyW/vfffw15V65c0dKhoaEgIiIiInKFPX6IiIiIiIiIiCyKDT9ERERERERERBblkaFerpbT1S+pbh5epWfu3n7u3DktbV4iPiEhQUvfvHnTkKd/rn6YGeB6SJp+aUVzWfRd+830r898TCIislu8eLGWNg+NnThxopZ+8MEHDXn169fX0uZ4UK9ePS198uRJQ17Dhg219L333mvI09fX5jpfP2S4du3ahjz9kGX9MvOAcVn4IUOGGPIOHjyopc3D1d5++20tXb58eRARERERucIeP0REREREREREFsWGHyIiIiIiIiIii2LDDxERERERERGRRXlkjh/9XA3meXX09EvyAkDlypWd7qdfel0/j475sTlPv9S7q+XVzfNE6FWrVs3w+MCBA06fyzl+iIjc88knn2jpxo0bG/L0c7dVrFjRkBcbG6ul9XO1AYCfn5+WDgsLM+SVKlVKS5uXiNfX12fOnDHkxcXFaWlzbCpbtqyWvnHjhiFPP99d//79DXnNmjVzeG5zHhERERFRRtjjh4iIiIiIiIjIotjwQ0RERERERERkUXl6qJd5WfQyZco4PAZgXKZdP3wLMC4Lb+4yrz+/qzxXS8sHBQUZHuvPby6LfkiafglgIiIyOnr0qJbWD9ECjHXrPffcY8hbvny5lp43b54hb9u2bVraPGRrxowZWvrKlSuGPP3S7/v37zfk6YdwmY+5Y8cOLX358mVDXvv27bW0fml3ADh//ryW1g8JA4xD2YoXLw4iIiIiIlfY44eIiIiIiIiIyKLY8ENEREREREREZFFs+CEiIiIiIiIisiiPzPGjZ15qV888r06lSpW0tH6uHADw9fXV0q6WejfnmedOcLafK4GBgYbH+nJfu3bNkKdfzt3VuYmICrrExEQtbZ4DR/+4QYMGhrx69epp6cqVKxvy9Euh79y505Cnn0foySefNOTt3bvX4fEB43xDTz/9tCFPX7aYmBhDXocOHRweHzAuV69/HwDXc84REREREZmxxw8RERERERERkUWx4YeIiIiIiIiIyKI8MtRLv/Stq+FUx48fNzxu0qSJlj527Jgh7+zZs1ra39/fkBceHq6lzUPL9F30zcur65/rakia+XxXr151eHzAONSLiIici4+P19L65dQB4MiRI1o6ICDAkPfHH39oaXMdrK/nz507Z8irXr2607Loz1GzZk1Dnn7Z+bCwMENeiRIltLR+iXbAGLeCgoIMeSdOnNDS+vcBMMZQIiIiIqKMsMcPEREREREREZFFseGHiIiIiIiIiMii2PBDRERERERERGRRHplwRj/Hgn75XMA4H4N5HgP9srgiYsjz8fFxeAzAuISueR6F1NRULW1eMlc/r4KXl7GNTF8289K+pUqV0tLmeSnuueceLe1q3iAiooJOP5dOo0aNDHmHDh3S0oULFzbkxcXFaWl9bACMc7Bt2LDBkFesWDEtvWzZMkOefnn1yMhIQ96mTZu0dLt27Qx5+hhgnreuSpUqWrpFixaGvH379mnpkJAQQ17FihVBREREROQu9vghIiIiIiIiIrIoNvwQEREREREREVmUR4Z62Ww2h2kAOHPmjJa+deuWIe8///lP7hbMpGjRom49Tz8EDTAOCVixYoUhTz90wTy0jIiI7MqVK6elly9fbsjTL3duHoq7a9cuLV26dGlD3rVr17S0eehVkSJFnJZFP0T5+vXrhjz9Y/PS6/rzmYf+6ocs+/r6GvL0S7/fddddhrzw8HCn5SQiIiIiMmOPHyIiIiIiIiIii2LDDxERERERERGRRbHhh4iIiIiIiIjIojwyx8+///6rpfVL6wJAbGyslh4xYsSdKlKOGjx4sJauUKGCIe/cuXNaWr+UPMB5G4iI9PRzok2ePNmQt3nzZqf79ezZU0tv3LjRkOft7a2l9fOxAcZ53aKjow15+iXj9fP2mB+b5xvSz1VnruOrVq2qpfXzEpkfly9f3pBnnhuPiIiIiMgV9vghIiIiIiIiIrIoNvwQEREREREREVmUR4Z6BQUFaemkpCRDXkhIiJZu2bKl28fUL4vr6W7wjz32mJb28fEx5KWkpNzp4hAR5UuFCtlDVLdu3Qx5pUqVcrpfjRo1HKbN+vbta3hcv359LW2OTfpl4c1DryIiIrR09erVDXn65z788MNOy6I/N2AcMla2bFlDnqdjHBERERHlL+zxQ0RERERERERkUWz4ISIiIiIiIiKyqCwP9UobWhUXF5fpffUrqSQmJhry4uPjHT4vo3PlpaFe+tdgHuqlX1HGLCvvZdo++tdvVdn5zN1JI0eOzNJ+o0ePzpHzm742Wdo/J95iT78PeVVB+s4COfe9Na+kdfPmTS2d1WObj6mvu69fv+70uebYpM8zl0Wfl5yc7LQs+nOb9zPHScYK1/JLrMgLPF1P55V4lRcVpO8swO9tXlGQPnf8zN052anrrVzP54TMfGdtksVv9qlTp9LNO0Cec/LkSZQpU8bTxchV/MyRlRSE7yzA721eUxA+d/zMkZUUhO8swO9tXlMQPnf8zJGVuPOdzXLDT2pqKs6cOYPg4GCP97ApyEQE8fHxKF26tGEyUCvKzmcuLi4OZcuWxcmTJw0TiN/JY1ihDFZ4DZ4uQ0H6zgKMFXlFQfrcMVZ4vgxWeA2eLkNB+s4CjBV5RUH63DFWeL4MVngNni5DZr6zWR7q5eXlZfmW4PwiNDTU00W4I3LiMxcSEpLlL3VOHcMKZbDCa/BkGQrKdxZgrMhLCsrnjrEi75TBCq/Bk2UoKN9ZgLEiLykonzvGirxTBiu8Bk+Wwd3vrLWbcomIiIiIiIiICjA2/BARERERERERWRQbfqhA8PX1xciRI+Hr6+uxY1ihDFZ4DXmlDESU91ilfvF0GazwGvJKGYgo77FK/eLpMljhNeSVMrgjy5M7ExERERERERFR3sYeP0REREREREREFsWGHyIiIiIiIiIii2LDDxERERERERGRRbHhh4iIiIiIiIjIotjwQ+RAUlIS+vbti2PHjnm6KJQHnDhxAo7mwRcRnDhxwgMlIqK8gLGC9BgriMgRxgrS81SsYMNPNl2+DJQoARw/7umS2DVqBMyd6+lSeN7BgwcxcOBAtGnTBm3atMHAgQNx8OBBt/YtXLgw5vJNzDFr1qxBcnJyuu3JyclYs2aNy32Tk5MRFRWFU6dO5VbxMlShQgVcvHgx3faYmBhUqFDBAyXK+2w2YMEC5/mrVqnnxMbeoQLlQQcPAqVKAfHx7u/TuzfQtavr55QvD0yYkLUyPfkk8OmnWds3v2KsyDsYK0jPnfpO7/hxFVd27Mid8njK8uVAtWpASoqnSwLMmAGEhWVuH31MunVLPf7nnxwt1h3BWJF3MFZkTYYNP+fOAf/9LxAZCfj6AmXLAg8/rCqhnOTuhWp0NPDoo0Dx4kBICNC9O3D+vPE527YB7dqpiqloUeD554GEBHt+TIx6DUFBQN26wPbtxv1fftn9C9/33wceeUSVX2/uXKBlSyA0VJ2nVi0gKkqdO6c4q3zfeQcYNgxITc25c+U3c+fORY0aNbB161bUrl0btWvXxrZt21CjRg23K96uXbtigas71wzkVut+XFwcFixYgP3797v1fG9vb1y4cCHd9suXL8Pb29vhPrt27XL7nztatWqFGAcf/qtXr6JVq1Yu9y1UqBDGjRvnsIK/U0QENpst3faEhAT4+fl5oESedfEi8OKLQLlyKi6UKgU8+CCwfr37x2jSBDh7VtWRrrh74b9mjarXS5d23ugkArz7LhARAfj7A23bAocPG58TEwP06KHiS1gY0K+fMX4cPw488AAQGKj+Nzf6P/SQ+w3vb72l4mtwsH3b//0fULu2ihthYSpGffihe8dLs2WLinuuOGt4e+cdFdeuXs3cOfMrxgo7xorss2KsyIn6Pi+ZNw9o317dHzhrJLpxQ90LFC2q6uLHHkt/r3HiBNC5MxAQoH4AfuMNQP/R275d1d9BQSo26T/WyclA/frA5s3ulXnoUFU3p30NU1KAsWOBqlVVLCtSBLj/fuDrrzP1VniEjw/w+uvAm296uiSZw1hhx1iRfZ6KFYVcZR4/DjRtqi4+x40DatYEkpKAP/5QFeKBA7lWLocSE1VlXbs2sGKF2jZihKpQN24EvLyAM2fUxfwTTwCffw7ExQGvvKJuHn75Re3z/vvqF9Zt24ApU4DnnrO3PG/cCGzaBEyalHF5rl0Dpk1T74fe8OHARx8Br74KfPCBuhE5fBiYOhWYNQsYPDin3hHHOnYE+vcHfv9dBaWCaOjQoXjrrbcQFRVl2D5y5EgMHToUjz32WIbHqFy5MqKiorB+/XrUr18fgYGBhvxBgwa53D+tdX/EiBGZfwE63bt3xwMPPICBAwfi+vXraNCgAY4fPw4RwezZszN8LY66EgLAzZs34ePj4zCvTp06sNlsTvdNy7PZbEhx4ycoZxXc5cuX072vjrRu3RqrV69GeXMLay4bMmQIAPV6R4wYgYCAAC0vJSUFmzZtQp06de5omfKCxx5Tv9rNnKl+FDh/Xv0YcPmy+8fw8VE3EM6kpKiLcnclJqrY0Lcv0K2b4+d8/LGq22fOBCpUUPHjwQeBffuAtDjbo4dqkPrrLxXv+vRRjSg//KDyX3sNuOsuVfe/8466gE2LLXPmqDjkRvWCEyeAxYuByZPt26ZPV/Fq0iSgRQvg5k1g1y5gzx733wdA/TDiSlKS87waNYCKFYHvvlNx3uoYK+wYK7LOyrEiJ+r7vCQxEWjWTP1w/Nxzjp/z6qvAkiXAzz+rHycGDlRxJa2xKyVFXV+XKgX8/beKGT17AoULq+t+QF2Ht26t4kL//mr7J5+ovE8/VfdXDRtmXN5169SP3vqv7+jRwFdfqfucBg3Uvc4//wBXrmT9fbmTevRQsXTvXuDeez1dGvcwVtgxVmSdp2OFy4afl15SF96bN6tfN9Pce6+6uE5z4oT61XL5cnXR26GDupgtWVLlR0cDQ4aoRpXERNVd8cMPVQMNoHrG/PuvqmhffVVtc/S5WL9eNUZt365+jQVUIAoPVw1BbduqC+nChYEvvlBlAVSDS61awJEjQKVKwP79qjt7lSrqgv5//1PPS0oCBgxQLeZOGiwNfvtN/frRqJF92+bNqnKfMMHYwFO+vOqFpP91dcoUFQROnlQ3Ie+8Azz7rD1//Hjgm2+Ao0dVa/7DD6sbl6Ag9Wttnz7qeWmf+5EjgVGjVNk7dQJmzy64DT9nz55Fz549021/5plnMG7cOLeOMW3aNISFhWHr1q3YunWrIc9ms2VYQQP21v1X0z7YWbBmzRoMHz4cADB//nyICGJjYzFz5ky89957TivoSbdbL202G77++msEBQVpeSkpKVizZg2qVq3qcN+c+jWh2+07cJvNht69e8PX19dQhl27dqFJkyYZHqdjx44YNmwYdu/e7TBYdunSJcNj1K1b12GQsNls8PPzQ6VKldC7d2/DLwXbb3cHFBHs3r3bENB8fHxQu3ZtvP766xme20piY4G1a1Ud1KKF2nb33Y4vYC9dUj00//hDNZZ8+imQ9qdatQpo1UpdqIaFqR6Mr7wCfPut6rF46BDwzDOqjgfs9dzKlSpmmHXsqP45I6Lq5XfeUb00AXWukiVV76Ann1SxYelS1WOmQQP1nMmTVX36ySeqEX//flU3V66sflBI+/PHxqpjp/0okZGfflINVXfdZd+2aJG6GenXz77N2UXxJ5+o9/PWLVX2CRNU7ANUvHnlFfUPUO/dl1+qHwOWLwcef9z+voaHq/979VJ/A0DFmtmzC0bDD2MFY4UZY4Wdu/W9q+tVwF6/z5mj/j95UjW+fPON6oEJqMaUN95QDeDe3qoeNN8LLF0KvPeeagz39gYaNwYmTlSN1e5Ku852NkXD1auqYf+HH1TDDaDKWa2auo9p1Aj480/1g8GyZSqG1KkDjBmjerGMGqV+2Ni/H/j+e3Wv8dRT6v4EUO/RtGmAqapwavZsdf+g7wSwaJG6R3v8cfu22rWN+2X0Xh0/ru495s5VcW7TJhXXpk5Vz00zY4bqKXvpkvqhpFkz43kyusdzJDxcNXzNnq3et/yAsYKxwiw/xgqnDT8xMarSeP99Y6NPmrQhRqmp6iI6KAhYvVp1X3z5ZdXjZtUq9ZyEBHXh/P77qqHk229VUDh4UHUdnTdPVVjPP++89R1Qv37abOoYafz8VAPPunWqkrl5U1W4aY0+gOoGCajnVKpk7zHUv7+6IalVS+V//LG6oUi74M/I2rWqq6be99+r9+Kllxzvk/a+zZ+vGoYmTLA3WPXpA5Qpo26GAPUaJk1SFfPRo+qYQ4eqC/gmTdS+776r3kfAHmABFZTHjnXvdVhRy5YtsXbtWlSqVMmwfd26dWjevLlbx8iJSiq7rfuA6rZYpEgRAMDSpUvx2GOPISAgAJ07d8Ybb7zhdL/PPvsMgKpcpk6dauh+6ePjg/Lly2Pq1KkO97377rszLJc7Qm+P4xERBAcHwz/ty3i7DI0aNcJzrr70t710+ws1fvz4dHnu/jrQoUMHTJkyBTVr1kTD21etW7Zswa5du9C7d2/s27cPbdu2xbx58/DI7ZaBlStXAgD69OmDiRMnIiStxbkACwpS/xYsUBfA+vrYbPRoVa+OG6cuLHv0UI38tz/O6Vy7pnpLfv216mIfEQFcv65+zfzmG/UcZ/tm5NgxNXRZfzEaGqq6x2/YoBpPNmxQdbQ+BrRtq+riTZtUI1bt2upiv317dfGfFj/eeEPFvrJl3SvP2rXpY02pUiqO/vuvurlyZuVK9d6sXKl+0HjiCXXj4eqrNGqUigkTJqibgC5d1C/IBw+qH1J0X000bKji9c2brv++VsBYwVhhxlhh52597+p6Nc21a6rBetYs9fxnnlEN599/r/I//VQ1MkyfrhoPPv1UXSunNb4AqmFhyBBV7yYkqGvgRx9Vw7X01/3ZsXWr+iFYHyuqVlX3Kxs2qPdhwwY1CiLtB25ANYq8+KLqxVK3rooVf/2l7juWL7fHigEDVFzUD/F1Ze1a4OmnjdtKlVL3MS+95LyHp7vv1fDh6u9SubJKP/WUiiuFCqm416+fasjp2lXdF44caTxPRvd4zjRsqF5bfsFYwVhhli9jhTixaZMIIDJvnrNnKH/+KeLtLXLihH3b3r1q382bne93770ikyfbH999t8hnn7k+14ULIiEhIoMHiyQmiiQkiAwcqM71/PPqOXv2iBQqJPLxxyI3b4rExIg89ph6zgcfqOfExoo89ZRIuXIiDzygynvokEjlyiKXLom88IJIhQoijz+unuvMI4+I9O1r3Naxo0itWq5fh4hIkyYizz1n3Pb44yKdOjnf5+efRYoWtT/+5huR0FDHz124UMTLSyQlJeOyWNGUKVOkePHi8vLLL8usWbNk1qxZ8vLLL0uJEiVkypQpsnDhQu1fRm7evCkHDhyQpKSkTJejfPnyTv9VqFDBrWNUrlxZ5syZIwkJCVK8eHFZvny5iIjs2LFDiuo/EE60bNlSYmJiMl12s71798rvv/9ueO/cef9SU1Old+/eEh8fn+0yZEf//v0lKioq3fYxY8ZI//79RUTk3Xfflfr167s8zsmTJ+XkyZO5Usb84pdfRMLDRfz8VF321lsiO3canwOIvPOO/XFCgtr2++/q8cqV6vGVK+rxN9+oxzt2GI/Tq5eqazMDEJk/37ht/Xq1/cwZ4/bHHxfp3l2l339fpEqV9McrXlzkyy9V+tQpkc6dRcqWVf+fOiWyerVIgwYily+r41WooOLIzZvOy1i7toj543jmjEijRqqcVaqo1z5njrEe79VLxcvkZONreOIJ+2NzPAVEXnnFeC7z+6+3c6fKO37cefmtgrHCjrFCYawwcqe+N3N0vQqIHDli3/bFFyIlS9ofR0Soa/c0SUkiZcq4rv8vXlTH3b1bPT52TD3evj3j1+Xsud9/L+Ljk/75990nMnSoSj/3nEj79sb8xER1vN9+U4/37FH3GOXKqXuOq1dFvv1WvZ5Tp9T+FSuKDB/uupyhoWo/vb17RapVU9f5NWuqeJN2XmecvVdff208LiCyf796/NRT6e9LnnjC+b1HGnfu8SZOFClf3vVx8hLGCjvGCiU/xgqnDT8bN7rX8OPsixsWJjJzpkrHx4u89ppI1aqqsggMVJXVG2/Yn+9Ow4+IyB9/iERGithsqsHpmWdE6tUTGTDA/pzvv1fBxNtbVd6vv64ejx3r/LitWoksWKBeT7t2IrduqQvsIUOc79O+vchLLxm3dejgXsNPeLjIjBnGbRMmqBuGNH/9JdK6tUjp0iJBQSroAiq4iLhu+PnzT/Xca9cyLosV2Ww2t/55eXk5PUZiYqL07dtXvL29xdvbW6Kjo0VEZODAgfLhhx/eqZciX3zxhRQqVEjCwsKkdu3aknL7LnDSpEnSsmXLXD9/dHS01KpVS3u/9O+dq/cvTUpKihQuXFgOHTqUI+W5fv16lvYLCQmRw4cPp9t++PBhCQkJERGR/fv3S1BQULrnpKSkyOjRoyUkJER73aGhoRIVFaX9PQqa69dVPRMVJdK4sapvv/nGng+I/PSTcZ+QEHtccNTw4+Mjkppq3CevNfyY3bihLnL/+Ufk1VfVjwG3bqm6e9Ik52WsUsV4k6O3e7e6KerRQ9X77drZG3969Up/IT5okIphaRw1/Hz3nXEfVw0/hw6pvH37nJffKhgrcg5jhXVjRUb1vTvXqwEBxmPOm6eu5UXUj6yAakTX69rVWP8fOiTy5JPqWjk4WN1PACJLlqj8vNTwY3bpkir3yZMijz4qMmqU+kGkWjWRRYucl9PHJ30sFVExYfNmVdc/+qj6m/TrZ893973S/0gfE2P8O9SpIzJ6tPG8EyYY7z2yeo/3v/+JlCjh/HXnNYwVOYexwnOxwmnHyMqV1bCqnJjA+fXXVXfNDz5Q3fp27FBdJG/dyvyx2rdX40kvXFDjTWfNAk6fVhPOpXn6adWl//RpNfncqFFqVQL9c/S++UZ173/kETU8rWtXNVfC44/bh6s5UqxY+onUqlRR3VxdTZ7pjuPH1eowtWqp8bdbt6p5iwD33reYGDVET999vyBJTU1165+rrnxvvfUWdu7ciVWrVhlmWG/bti3mzJmTqfLcunULBw8ezNIM8i+99BI2bNiA6dOnY926dfC63Uc3MjIS7733nlvHOHXqFL788ksMGzYMQ4YMMfzLyODBg1GhQgVcuHABAQEB2Lt3L9asWYMGDRpglasvyG1eXl6oXLkyLmdjJsiUlBSMGTMGd911F4KCgnD06FEAwIgRIzBt2jS3juHn54e///473fa///5b+/umpqY6nE1/+PDh+PzzzzF27Fhs374d27dvxwcffIDJkydne5K9/MrPT807MGKEmtyyd+/0XcDT5pxJY7O5Xm3Q3z9zEzpnRtpE0uaVWc6ft+eVKqVii15ysqpPnU1E/cEHKi7Vr6/ixWOPqdfdrVvm40eaGjVUF/7vvlNDBf76Sw0BS5PZ9xVwPGTbmbSFMjKaJNoKGCuMGCsYKxxxVd+7e73qqN5yMs+rU2mrY/3f/6lhSJs2pT9PdpUqpY5nXvHQHCscxZK0PEeGDFHzG5Upo2LD44+rerlz56zFCi8v4L771DHnzVPD5KZNU8OaAfffK/3fJS3+ZmZV4Kze48XE5K8Yw1hhxFiRT2OFq1ahDh1E7rpLtUibpf1K6Gqo15Yt6nGNGsYu7fHxqlV48GD7tsqVRT75JPMtV8uXq18MDhxw/pxp09QvDY5+2bxwQfVYOnVKPe7SRbVmi6hfjGvXdn7ccePS56f1lEo7hllaGZwN9ercWaV/+UWkcGFjF/8xY4y/0H7/vfplxZF33hFp1sx52Slj5cqVkw0bNoiISFBQkNYyf/jwYQkODnbrGLnRup+cnCzbt293u5vlsmXLJCAgQGrUqCGFChWSOnXqSFhYmISGhkorfTcBJ4oWLSo7b/frDgkJkQO3v2zLly+XOnXquFWGRYsWSbNmzWR3Wh/jTBo9erRERkbKd999J/7+/tr7OHv2bGnUqJFbxxgzZoz4+/vLoEGDtG66gwYNkoCAAHnvvfdERGT8+PHStm3bdPtGREQ47H66YMECKV26dJZek9V8+qmxa7+jXjehofZfiR31+HHUg/G550QeeihzZXF07tRUkVKljHHm6lURX1+RH39Uj/ftU/v+84/9OX/8oWLM6dPpz7Nvn0ilSvYYWauW6jkqon7ddNVT6eWX3evJdPmyKtOvv6rHjnpADR4s0qKF/bGjHj/OekBdupT+nF9/rYZYkHsYKxTGioITK/T1vTvXq47q9/nz1XPSOBrqVbasvb67dEk9f80a+3PWrjXWbznR4yc2Vr2eX36xbztwQD339tdcfvtN9Wo5f97+nK++Ur1ab9xIf65ly1SPobT3KCTEPqx58GDj/ZBZ586u89Ns3WofypXV9+rKFbVt5Ur12NFQryefNP4t3bnHc9Tj55ln1L+ChLFCYazwXKxw2fATHa0ulKtXVxXgoUPqQnfiRNWlT0RdTNepI9K8uap0Nm0SqV/feBH66KPqOdu3q4ru4YdVt0N9pdCunWp0OXVKjUN1Zvp0VfEeOSIya5ZIkSLph2NNnqzKcvCgyOefi/j7qzI78vTTxnGoH32kyr9vn5qvxzyUS2/XLjWfkPl7MnSoagx74w2Rv/9W8yQsWybyn/8YG5UKF1bDBw4dUkHU29te2e7YYW9Aio5W43vvussYSNMu3JctU+9ZWpdaEfX+Oxh2WKCsWrVKHnroIalYsaJUrFhRHn74YVmjj4IZ0FcE+gp6x44dWhe+jAwaNEjq168va9eulcDAQO0YCxYscLtyGzx4sHx9exB2cnKyNG3aVGw2mwQGBsrKtA+MC/fdd5+8++67htcRHx8vXbp0kS+djV/RCQsLk6NHj4qISGRkpKxYsUJERI4cOSL+/v5uvYawsDDx8fERLy8v8fPzk/DwcMO/jFSsWFGWLVtmeA0iqgtlWFiYW2UQEfnuu++kUaNG2nkbNWok33//vZZ/7do1h10+fX195eDBg+m2HzhwQPz8/Nw+vxVcuqSGFc2apeZ5OHpUdUMvWdI451lONfy8/76aI+HAAVXP3brluFzx8SrGbN+ujjt+vEr/+6/9OWPHqmHICxeq+vuRR1Q3eP2fvEMHkbp1VSxbt079KPHUU+nPl5qqGtfTGmRERF58UV2k79unjuFsKJeI6tpfooRxrp4BA1S9vW6dihsbNqjjFS9ub6DJqYafU6dUg9aMGeoHEP1Q+V690s9fZ2WMFQpjhR1jheJOfe/O9ao7DT9jx6pr+vnz1Rwzzz2n7hXS6ruUFNXY9MwzIocPqx9+77sv8w0/ly+r/CVL1HNnz1aPz561P2fAABV3VqxQPwQ0bqz+pUlOVg0e7dur1790qaqn33or/fmuX1f3TPoydeyoXt+OHaqR3dFQrjSTJqn7Er3HHlMxbuNGFStWrlTzw1WpohrMsvpemRt+NmxQDVzjxql7lcmTVQzV/y3ducdz1PBz993p5y7K6xgrFMYKu/wWK1w2/Iio+RBefll9QX18VGXepYu9UhBRF9ZduqhxncHBqufKuXP2/GPHVODw91et959/ri5S9ZXChg3q11JfX2MgMHvzTRVwChdWF+Sffpp+Tohnn1XBw8dHHdNZxbJ0qUjDhsZfKRITVfmDg0XatDG25jvSsKHI1Knpt8+ZoyZ1SxtXW6uWuqDX9zr68ks1X1HhwqqyNpdz/Hj1C4i/v8iDD6p885wMAwaoyh0QGTlSbTt1Sh3TAnMKZtmsWbOkUKFC0r17d5k4caJMnDhRunfvLoULFzZ8IV1p3ry5TLo9SUdQUJBWSQ0cOFAefPBBt46RE637d911l2y53X1u/vz5Urp0aTl48KC888470qRJkwz3DwoKkiO3Z1QMCwuTPXv2iIgKNHfffXeG+zdr1kzm375SeOqpp6RDhw6ybt066dmzp9x7771uvYYZM2a4/JcRPz8/OX57pln9+7h3714JDAx0qwzZ0bBhQ/nvf/+bbvvAgQPl/vvvz/Xz5yU3bogMG6bmVgsNVb0p77lH9TLUzymWUw0/Fy6oHwaCgowXpGZpxzP/69XL/pzUVJERI1QM8fVVdbw57l6+rBp6goLUr7J9+hgbRdJMnaouvvXOn1fHTIuD+sZ4s6QkNR/G0qX2bb/8on5djYhQ8at0aXWOXbvsz8mphh8RFZNKlVINQGnv0/Xr6u+Q9su21TFW2DFWZJ/VYoW79X1G16vuNPwkJam6LCRENS4MGSLSs6exvvvrLzUnjq+vuq5etSrzDT9pE02b/6VdQ4uoevCll9R8nAEBqnFD3zAkohpcOnZUr7lYMTXPjaO5eocNU3l6hw+rhpiQEPWDgaspPS5fVnMm6Uc2/O9/6r6qeHEVK8qVE+nd2zghf1beK3PDj4gaNVGmjHqdDz+ses3q/5bu3OOZY9Lff6u/cX6ah5Sxwo6xIvs8FSsybPgh1xYvVhVrXpqzb+jQ9MPICpqqVavK+PHj023/9NNPpWpad7UMrF27VoKCgmTAgAHi5+cngwcPlnbt2klgYKD8ox8L4kJOtO77+vpqs70/99xzMvh2ND169KhblXzJkiVl3+1ZWqtVq6Z1LdyxY4dbldvSpUtl7ty5IqICyz333CM2m02KFSumrQSQ2+rVqyezZs0SEeP7OHr0aGl2B8Y0rlq1SgIDA6VatWrSt29f6du3r1SrVk2CgoIy9WsPkd7nn6efINTTvvxSNbQVFIwVdowV2cdYQbnh9dftqxdbQffuqjdvfsJYYcdYkX2eihWFcm/2oIKhc2fg8GE1kXTZsp4ujVKihJpEriA7evQoHn744XTbu3TpgrffftutYzRr1gw7duzA2LFjUbNmTfz555+oV68eNmzYgJo1a7p1jAYNGmDJkiX473//CwCw3Z457+uvv0bjxo3dOkbJkiWxb98+REREYOnSpZgyZQoA4Nq1a/D29s5w/0aNGmHdunWoVq0aOnXqhNdeew27d+/GvHnz0KhRowz3f/DBB7V0pUqVcODAAcTExCA8PFx7PY7ExcUhJCRES7uS9jxn3n33XfTq1QunT59Gamoq5s2bh4MHD+Lbb7/F4sWLM3wNgJrI7bPPPsNPP/2EEydO4JZp5sGYtBltHWjRogUOHTqEL774Agduz3jfrVs3vPTSSyhdurRb5ycye+EFNYFofDwQHOzp0iiFCwOTJ3u6FHcOY4UdY4XCWEF5zfDhwJdfqkmXvZwuy5M/3LqlJn9+9VVPlyRzGCvsGCuUfBkrcq1JiciDKlasKFMdjMGbMmWKVKpU6Y6VIyda90eOHCmhoaFStWpVKVeunNy4PXPgtGnT3JqALDo6WptELSEhQV544QWpWbOmdOvWTevmmBu8vLzk/O2xkvplGvX/Mlr6Um/NmjXStm1bKV68uPj7+0vTpk3ljz/+cLs8I0aMkIiICPnkk0/Ez89PxowZI/369ZOiRYvKRGeTgBGRpTFW2DFWKIwVRGTGWGHHWKHkx1hhE8nsYopEed+UKVPwyiuvoG/fvmjSpAkAYP369ZgxYwYmTpyIF154IcNjbNu2DYULF9Za4RcuXIhvvvkG1atXx6hRo+Dj4+NWWaKjozF27Fjs3LkTCQkJqFevHt588023W/cB4JdffsHJkyfx+OOPo0yZMgCAmTNnIiwsDI888ojbx3FXt27d3H7uvHnzHG5fvXo1mjZtikKFCmG1fi1qB1q0aOEy/9SpU9rrNtu4caNbvzBUrFgRkyZNQufOnREcHIwdO3Zo2zZu3IgffvjB5f6xsbGYNm0a9u/fDwC499570bdvX4SGhmZ4biLKmxgrsoexIj3GCiLrYazIHsaK9DwRK9jwQ5Y1f/58fPrpp9oXqlq1anjjjTfcrtDuu+8+DBs2DI899hiOHj2K6tWro1u3btiyZQs6d+6MCRMm5GLpHbtx4wb8/PyytO/WrVsNlUvdunWdPrdPnz5aWkQwf/58hIaGokGDBtqxYmNj0a1bN3zzzTdZKk9mVK9eHevWrUORIkUM29evX4/OnTsjNjY2w2MEBgZi//79KFeuHCIiIrBkyRLUq1cPR48eRd26dXH16lWn+/7zzz948MEH4e/vj4YNGwIAtmzZguvXr2tddYkof2KsMGKsYKwgovQYK4wYK/JhrPBgbyOiPC0kJESbtX7s2LHS/vYsrOvWrZMyZcq4dYw2bdrIN998I1evXs1yOZKTkyUqKkpKly4t3t7e2gRk77zzjrYcoyvnz5+XVq1aic1m05YbtNls0rp1a7lw4UKG+w8dOlT69+8vybp1p5OTk+X555+X119/3e3XceXKFfnjjz9k1qxZMnPmTMO/jPTp00fq168vcXFx2rbVq1dLSEiIw8n2HKlSpYps3LhRRESaNm0qH374oYiIzJ49W4oXL+5y32bNmknv3r0lSbdkR1JSkvTq1UuaN2/u1vmJyJoYKxTGCsYKInKOsUJhrPBcrGDDD1lShQoV5NKlS+m2X7lyRSpUqODWMYKDg+XQoUMiItK2bVuZMGGCiIj8+++/4ufn59YxBg0aJKVKlRJ/f3/5z3/+IwsWLJBbt265+SqU0aNHS2RkpHz33XeG2fxnz57t1ljc7t27S4MGDbQZ+EXUcoUNGjSQJ598MsP9ixUrJgf064jeduDAASlSpIhbr2HRokUSHBwsNptNQkNDJSwsTPsXHh6e4f4pKSny6KOPSosWLeTGjRuyYsUKCQoK0v4m7njzzTfl/dvLSMyePVsKFSoklSpVEh8fH3nzzTdd7uvn5yf79+9Pt33v3r3i7+/vdhmIKG9hrLBjrFAYK4jIjLHCjrFCyY+xgg0/ZEk2m02bAEzv3Llz4uPj49YxWrVqJT179pRvv/1WChcuLIcPHxYRtQTf3Xff7XZZUlJS5I8//pBevXpJSEiIhIeHy3PPPSerVq1ya/+KFSvKsmXLRMS45OD+/fslLCwsw/1DQkJk8+bN6bZv2rRJQkNDM9w/LCxMFixYkG77ggUL3Dq/iEjlypVl8ODBkpiY6NbzHbl586a0bdtWmjRpIkFBQTJ58uQsH0tEZMOGDfLpp5/KokWLMnxuiRIlHE74tnTpUilRokS2ykFEnsNYYcdY4RhjBRExVtgxVjiWH2IFl3MnS1m0aJGW/uOPPwwTZKWkpGD58uUoX768W8eaMGECevTogQULFmD48OGoVKkSADUhWtrEbu7w8vJC+/bt0b59e0ydOhW//vor3n//fUybNg0pKSkZ7n/69Gnt3HqpqalISkrKcP/U1FQULlw43fbChQsjNTU1w/379OmDfv36ITo6WhuHumnTJowdO9YwZteV06dPY9CgQQgICHDr+QCwa9eudNtGjRqFp556Cs888wweeOAB7Tm1atXK8HiXL19G0aJFAQAnT57Eb7/9huvXr2vji1154okn0K9fP3zyySeGSf3eeOMNPPXUU26/JiLKGxgr0mOsUBgriCgNY0V6jBVKvowVudakROQBNptNW8ovLZ32z8fHR6pUqSK//vprts5x/fr1THerFBE5e/asfPbZZ1K/fn2x2Wxy//33u7VfvXr1ZNasWSJibJkfPXq0NGvWLMP9u3TpIg888ICcPn1a23bq1Clp0aKFdO3aNcP9U1JS5KOPPpLSpUtr72Xp0qXlo48+MozPdeXRRx+VOXPmuPXcNI7+jvrH7i7buGvXLrn77rvFy8tL7rnnHtm+fbuULFlSgoKCJCQkRLy9vWX+/Pkuj3Hz5k0ZNGiQ+Pj4aEtG+vr6yiuvvKItg0lE+QdjRXqMFYwVRGTEWJEeY0X+jRVc1YssqUKFCtiyZQuKFSvm0XLExcVh7ty5+OGHH7Bq1SpERkaiR48e6NGjBypWrOjWMRYuXIhevXrhrbfeQlRUFEaPHo2DBw/i22+/xeLFi9GuXTuX+588eRJdunTB3r17UbZsWW1bjRo1sGjRIqfLGTp7PQAQEhKS4XP1v5JcvHgRUVFR6NOnD2rWrJnul4IuXbqk2//ff/91u1x3332307yOHTuiUKFCGDZsGGbNmoXFixfjwQcfxP/93/8BAP773/9i69at2LhxY4bnuXbtGqKjowGoZRwz80sDEeU9jBV2jBWMFUTkGGOFHWNF/o0VbPghS+nZsyceeeQRPPjggwgKCsrWsby8vGCz2Zzmu9Od0t/fH+Hh4XjiiSfQo0cPt7r/ObJ27VpERUVh586dSEhIQL169fDuu++iffv2bu0vIli2bBkOHDgAQC1B2bZt2yyVxV1eXl5uPc9ms7n1XmZVsWLFsGLFCtSqVQsJCQkICQnBli1bUL9+fQDAgQMH0KhRI7eWbgRUcAOgBTsiyn8YKxxjrGCsICI7xgrHGCvyZ6zgHD9kKZUqVcIHH3yAZ555Bi1btkSXLl3QpUsX3HXXXZk+1vz58w2Pk5KSsH37dsycOROjR4926xiLFi1CmzZt3K6szJKTk/HBBx+gb9+++OuvvzK174oVKzBw4EBs3LgRISEhaNeundaKf/XqVdx7772YOnUqmjdv7vI458+fx+uvv47ly5fjwoULMLcVO6tc3Rnnm1n79u3DiRMncOvWLcN2Ry37aWJiYlCqVCkAQFBQEAIDAxEeHq7lh4eHIz4+3uV5k5OTMXr0aEyaNAkJCQnasf773/9i5MiRDsc6E1HexVhhx1ihMFYQkRljhR1jhZKvY0WuDSIj8qATJ07IF198Ie3btxdfX1+pV6+ejB49WrZv357tY3///ffSpUuXTO1z4cIFWbt2raxdu1YuXLiQqX0DAwPl2LFjmdpHROThhx+W8ePHO82fOHGiW2NxO3ToINWrV5cvv/xS5s+fLwsWLDD8c+Xvv/9ON/Z55syZUr58eSlevLg899xzbo1ljY6Ollq1ajkcj5vRWFybzWZ4z4OCguTo0aPa43PnzmV4jAEDBkiJEiVk6tSpsnPnTtm5c6dMnTpVSpUqJQMGDMiw/ESUNzFWMFakYawgImcYKxgr0uTnWMGGH7K8uLg4mTNnjjz99NMSHh4u5cqVk5dffln27NmTpeNFR0dLYGCgW89NTEyUPn36iLe3t1apFCpUSPr27ev2EoRdunSRGTNmZLqc5cqVk3379jnN379/v5QtWzbD4wQFBWU5sD344IMyduxY7fGuXbukUKFC0r9/f/n000+lVKlSMnLkyAyP89BDD8kjjzwiFy9elKCgINm3b5+sXbtWGjZsKGvWrHG5r81mk06dOsmjjz4qjz76qBQqVEjat2+vPe7UqVOGFXRISIj89ttv6bYvWbJEQkJCMiw/EeV9jBWOMVYwVhCRHWOFY4wVeT9WcKgXWV5wcDC6d++O7t27IyUlBatXr8bChQuxYcMG3HvvvZk61vXr1zFp0iS3u3i++uqrWL16NX799Vc0bdoUALBu3ToMGjQIr732GqZMmZLhMTp27Ihhw4Zh9+7dqF+/PgIDAw35zrojnj9/3mVXwUKFCuHixYsZnr9s2bLpumG6a+fOnXjvvfe0x7Nnz8b999+vTYBWtmxZjBw5EqNGjXJ5nA0bNmDFihUoVqwYvLy84OXlhWbNmuHDDz/EoEGDsH37dqf79urVy/D4mWeeSfecnj17ujy/r6+vw+U6K1SoAB8fH5f7ElH+wFjhGGOFHWMFETFWOMZYYZdnY0WuNSkReVCrVq3kypUr6bZfvXpVWrVq5dYxwsLCJDw8XPsXFhYm3t7eEhwcLAsXLnTrGEWLFpWVK1em275ixQopVqyYW8cwLx9pXobQmcjISJfLCc6dO1cqVKiQ4fn/+OMPad++fZa6hfr6+sqJEye0x02bNpX33ntPe3zs2DEJCgrK8DhhYWFaN8rIyEhZsWKFiIgcOXJE/P39M12uzBo9erQ89dRThu6jN27ckB49esioUaNy/fxElDsYKxgrchJjBZE1MVYwVuQkT8UK9vghS1q1alW6iboA4MaNG1i7dq1bx5gwYYLhsZeXF4oXL47777/fMImXK9euXUPJkiXTbS9RogSuXbvm1jGyOplZp06dMGLECHTo0AF+fn6GvOvXr2PkyJF46KGHMjzOE088gWvXrmnLDJpb+2NiYpzuW7JkSRw7dgxly5bFrVu3sG3bNsMEdvHx8W5NYFajRg3s3LkTFSpUwP3334+PP/4YPj4++N///ofIyMgM98+Kbt26GR4vW7YMZcqUQe3atQGoXx1u3bqFNm3a5Mr5iSj3MVYwVmQXYwWR9TFWMFZkV16IFWz4IUvZtWuXlt63bx/OnTunPU5JScHSpUvd7k5p7sqXFY0bN8bIkSPx7bffapXk9evXMXr0aDRu3DjD/VNTUzFjxgzMmzcPx48fh81mQ2RkJB577DE8++yzLpeFfOeddzBv3jxUqVIFAwcOxD333ANALTP4xRdfICUlBcOHD8+wDOZAlRmdOnXCsGHD8NFHH2HBggUICAgwzPa/a9cuVKxYMcPjvPPOO0hMTAQAREVF4aGHHkLz5s1RtGhRzJkzJ8vlcyU0NNTw+LHHHjM85hK9RPkXY4UdY0X2MFYQWRdjhR1jRfbkiViRa32JiDxAPyO7oy6MAQEBMm3aNLePd+XKFfnkk0+kX79+0q9fPxk/frzExsa6vf/u3buldOnSUrRoUWndurW0bt1aihYtKnfddVeGk8ClpqZK586dxWazSZ06deTJJ5+UJ554QpuF/pFHHsnw/MePH5eOHTumm7G+Y8eOhhnoc8vFixelefPmYrPZJDg4WObNm2fIb926tbz99ttZOvbly5clNTU1J4pJRAUMY4URYwURUXqMFUaMFfmbTSSLsysR5UH//vsvRASRkZHYvHkzihcvruX5+PigRIkS8Pb2dutY//zzDx588EH4+/ujYcOGAIAtW7bg+vXr+PPPP1GvXj23jnPt2jV8//33OHDgAACgWrVq6NGjB/z9/V3u980332Dw4MFYuHAhWrVqZchbsWIFunbtis8//zzDCcQA4MqVKzhy5AhEBJUrV3a7S6nZjRs30nV1DQkJyXC/q1evIigoKN17HxMTg6CgILcnMjty5Aiio6PxwAMPwN/fHyLi8tcJIiJHGCscY6wgIrJjrHCMsSKf8lCDE1GuuXXrlvTu3TvbLc/NmjWT3r17S1JSkrYtKSlJevXqJc2bN89uMTPUrl07+fDDD53mv//++9K+fftcL0dCQoK8/PLLUrx4ce1XD/2/O+HSpUvSunVr7ZeF6OhoERHp06ePDBky5I6UgYishbEiZzFWEJEVMVbkLMYKz2GPH7KksLAwbN++HRUqVMjyMfz9/bF9+3ZUrVrVsH3fvn1o0KCB00nUFi1a5PY5nC2ZCAClSpXC0qVLUadOHYf527dvR8eOHQ3jjXPDyy+/jJUrV2LMmDF49tln8cUXX+D06dP46quvMHbsWPTo0SNXzw+oZREvXLiAr7/+GtWqVcPOnTsRGRmJP/74A0OGDMHevXtzvQxEZD2MFTmHsYKIrIqxIucwVniQp1ueiHJDz549Zfz48dk6RokSJeSPP/5It33p0qVSokQJp/u5WibR3SUTRUQKFy4sZ86ccZp/+vRp8fHxcf8FZVHZsmW1pSODg4Pl8OHDIiLy7bffSseOHXP9/CIiJUuWlB07doiISFBQkNYyHx0dLYGBgXekDERkPYwVOYexgoisirEi5zBWeA5X9SJLqly5MqKiorB+/XrUr18fgYGBhvxBgwZleIwnnngC/fr1wyeffIImTZoAANavX4833ngDTz31lNP9srpMollKSgoKFXL+FfX29kZycnKOnMuVmJgYbWnDkJAQbZnFZs2a4cUXX8z18wNAYmIiAgICHJbN19f3jpQhzY0bN9ItY0lE+RNjRc5hrDBirCCyDsaKnMNYYXQnYwUbfsiSpk2bhrCwMGzduhVbt2415NlsNrcq6E8++QQ2mw09e/bUKsLChQvjxRdfxNixY3Ol3Hoigt69ezutgG7evJnrZQCAyMhIHDt2DOXKlUPVqlXx008/oWHDhvj1118RFhZ2R8rQvHlzfPvttxgzZgwA9TdMTU3Fxx9/nG6CutyQmpqK999/H1OnTsX58+dx6NAhREZGYsSIEShfvjz69euX62UgopzHWJFzGCsYK4isirEi5zBWeC5WcI4fIgdSUlKwfv161KxZE76+voiOjgYAVKxY0WELsd6kSZPcPo+rQNGnTx+3jvHNN9+4fb6s+Oyzz+Dt7Y1BgwZh2bJlePjhhyEiSEpKwvjx4zF48OBcPT8A7NmzB23atEG9evWwYsUKdOnSBXv37kVMTAzWr1+PihUr5ur5o6KiMHPmTERFReG5557Dnj17EBkZiTlz5mDChAnYsGFDrp6fiPImxgo7xgrGCiJyjLHCjrHCc7GCDT9ETvj5+WH//v2ZnsjN3efbbDYcPXo0K0XzqH///Rdbt25FpUqVUKtWrTt23qtXr+Lzzz/Hzp07kZCQgHr16uHll19GRERErp+7UqVK+Oqrr9CmTRsEBwdrk8AdOHAAjRs3xpUrV3K9DESUNzFWOMZYwVhBRHaMFY4xVty5WMGhXmQZQ4YMwZgxYxAYGIghQ4a4fO748eMzPF6NGjVw9OjRTFfQx44dy9Tz86oNGzbg8uXLeOihh7Rt3377LUaOHInExER07doVkydPvmNjYUNDQzF8+PA7ci6z06dPo1KlSum2p6amIikpyQMlIqKsYqzIWYwVdowVRNbBWJGzGCvsPBUr2PBDlrF9+3bty7J9+3anz7PZbG4d77333sPrr7+OMWPGOJzILSQkJOuFzQeioqLQsmVLrYLevXs3+vXrh969e6N69er4+OOPUbp0aYwaNeqOlCc2NhabN2/GhQsX0k1017Nnz1w9d/Xq1bF27Vrcfffdhu2//PIL6tatm6vnJqKcxViRsxgr7BgriKyDsSJnMVbYeSpWcKgXkUlUVBRee+01BAcHa9v0lbqIwGazISUlJcNj9e3b12X+9OnTs17QXBYREYFff/0VDRo0AAAMHz4cq1evxrp16wAAP//8M0aOHIl9+/blell+/fVX9OjRAwkJCQgJCTH8PWw2m7YiQG5ZuHAhevXqhbfeegtRUVEYPXo0Dh48iG+//RaLFy9Gu3btcvX8RJT3MFYojBV2jBVEZMZYoTBW2HkqVrDhh8jE29sbZ8+exf79+10+r0WLFhke69FHHzU8TkpKwp49exAbG4vWrVtj3rx52SprbvLz88Phw4dRtmxZAGqZxY4dO2rdIo8fP46aNWsiPj4+18tSpUoVdOrUCR988EGGk+DllrVr1yIqKsowFvjdd99F+/btPVIeIvIsxgqFscKIsYKI9BgrFMYKI0/ECg71IktKTEzE2LFjsXz5codd+FxNfpbWFupOBZyR+fPnp9uWmpqKF198MddnjM+ukiVL4tixYyhbtixu3bqFbdu2YfTo0Vp+fHw8ChcufEfKcvr0aQwaNMhjlTOgln7866+/PHZ+Isp5jBXZx1hhxFhBZD2MFdnHWGHkiVjBhh+ypP79+2P16tV49tlnERER4fb42zSZfX5meHl5YciQIWjZsiWGDh2aa+fJrk6dOmHYsGH46KOPsGDBAgQEBKB58+Za/q5du+5YkHnwwQfxzz//IDIy8o6cj4gKBsaK7GOsICKrY6zIPsYKz2PDD1nS77//jiVLlqBp06ZZ2r9KlSoZVtLZGf8ZHR2N5OTkLO9/J4wZMwbdunVDixYtEBQUhJkzZ8LHx0fLnz59+h3rut65c2e88cYb2LdvH2rWrJnuF4EuXbrk+DnDw8PdDtS5PRaYiHIHY0X2MVYwVhBZHWNF9jFWeD5WsOGHLCk8PBxFihTJ8v6jR49GaGhotsthXv5RRHD27FksWbIEvXr1yvbxc1OxYsWwZs0aXL16FUFBQfD29jbk//zzzwgKCrojZXnuuecAqAnyzNydEC+zJkyYkOPHJKK8hbEi+xgrJuT4MYkob2GsyD7Gigk5fszM4uTOZEnfffcdFi5ciJkzZ2Z6/KaXlxfOnTuHEiVKZLscrVq1gs1m08b3enl5oXjx4mjdujX69u2LQoXY9prfXb9+Hf7+/p4uBhFlAWMF3SmMFUT5F2MF3Sm5GSv46SDLqFu3rqEL3ZEjR1CyZEmUL18+XRe+bdu2OT1OTozDTU1Nxbhx43Dz5k0kJSWhdevWGDVqFC/6MmnDhg24fPkyHnroIW3bt99+i5EjRyIxMRFdu3bF5MmT4evrm6vlGDRoECZNmpRue2JiIh566CGsXLkyV89PRDmHscJ6GCuIKKcxVlhPQY8VbPghy+jatWuOHCcnOsG9//77GDVqFNq2bQt/f39MmjQJFy9exPTp03OghAVHVFQUWrZsqVXQu3fvRr9+/dC7d29Uq1YN48aNQ+nSpTFq1KhcLceSJUsQHh5uWH0gMTERHTp0yNXzElHOY6ywHsYKIsppjBXWU+BjhRBRjqtUqZJMnTpVe/zXX3+Jj4+PpKSkeLBU+U+pUqVky5Yt2uO3335bmjZtqj3+6aefpFq1arlejiNHjkhERIR89tlnIiISFxcnjRs3lubNm0tCQkKun5+IrImxImcwVhCRlTFW5IyCHivY44cs6eTJk7DZbChTpgwAYPPmzfjhhx9QvXp1PP/887l+/hMnTqBTp07a47Zt28Jms+HMmTNamShjV65cQcmSJbXHq1evRseOHbXH9913H06ePJnr5ahYsSKWLl2KVq1awcvLCz/++CN8fX2xZMkSBAYG5vr5iSh3MFZYA2MFEeUmxgprKOixwivXjkzkQU8//bQ2PvLcuXNo27YtNm/ejOHDhzucwT2nJScnw8/Pz7CtcOHCSEpKyvVzW0nJkiVx7NgxAMCtW7ewbds2NGrUSMuPj49PN846t9SqVQuLFy/G22+/jYCAAPz++++8kCfK5xgrrIGxgohyE2OFNRT0WMEeP2RJe/bsQcOGDQEAP/30E2rWrIn169fjzz//xIABA/Duu+/m6vlFBL179zZMDnbjxg0MGDDA8KWeN29erpYjv+vUqROGDRuGjz76CAsWLEBAQACaN2+u5e/atQsVK1bMlXObJ/VL4+vrizNnzqBp06baNleT+hFR3sVYYQ2MFUSUmxgrrKGgxwo2/JAlJSUlaZXjsmXL0KVLFwBA1apVcfbs2Vw/f69epGzmXwAAstlJREFUvdJte+aZZ3L9vFYzZswYdOvWDS1atEBQUBBmzpwJHx8fLX/69Olo3759rpw7pyb1I6K8i7HCGhgriCg3MVZYQ0GPFTaRHJhqnCiPuf/++9GqVSt07twZ7du3x8aNG1G7dm1s3LgR//nPf3Dq1ClPF5Ey4erVqwgKCoK3t7dhe0xMDIKCggyVNhGRuxgrrIWxgohyA2OFtRTUWMGGH7KkVatW4dFHH0VcXBx69eqlLXf49ttv48CBA+wKSVly69YtXLhwAampqYbt5cqV81CJiCg7GCsoNzBWEFkLYwXlhjsdK9jwQ5aVkpKCuLg4hIeHa9uOHz+OgIAAlChRwoMlo/zm0KFD6NevH/7++2/DdhGBzWZDSkqKh0pGRNnFWEE5hbGCyLoYKyineCpWcI4fsixvb2+Eh4dj7NixGDBgAMLCwlC+fHlPF4vyoT59+qBQoUJYvHgxIiIiHE7ORkT5E2MF5RTGCiLrYqygnOKpWMEeP2R5ISEh2LFjByIjIz1dFMqnAgMDsXXrVlStWtXTRSGiXMJYQdnFWEFkfYwVlF2eihVed/RsRB7Atk3KrurVq+PSpUueLgYR5SLGCsouxgoi62OsoOzyVKxgww9ZSlRUFK5du+bpYpDFfPTRRxg6dChWrVqFy5cvIy4uzvCPiPIXxgrKDYwVRNbCWEG5wVOxgkO9yFK8vb1x9uxZwyRrJ0+eROnSpdMt2UfkLi8v1UZuHoPLCTuJ8ifGCsoNjBVE1sJYQbnBU7GCkzuTpThqxyxbtqwHSkJWsnLlSk8XgYhyEGMF5QbGCiJrYayg3OCpWMGGH7IcrqJBOa1FixZO8/bs2XMHS0JEOYWxgnIaYwWR9TBWUE7zVKzgUC+yFC8vL4SGhmZYScfExNyhEpEVxcfH48cff8TXX3+NrVu3svs+UT7DWEF3AmMFUf7GWEF3wp2KFezxQ5YzevRohIaGeroYZEFr1qzBtGnTMHfuXJQuXRrdunXDF1984eliEVEWMFZQbmGsILIOxgrKLXc6VrDhhyznySefNEzCRpQd586dw4wZMzBt2jTExcWhe/fuuHnzJhYsWIDq1at7unhElEWMFZSTGCuIrImxgnKSJ2MFl3MnS+E4XMpJDz/8MO655x7s2rULEyZMwJkzZzB58mRPF4uIsomxgnISYwWRNTFWUE7ydKxgjx+yFE5ZRTnp999/x6BBg/Diiy+icuXKni4OEeUQxgrKSYwVRNbEWEE5ydOxgj1+yFJSU1PZHZNyzLp16xAfH4/69evj/vvvx+eff45Lly55ulhElE2MFZSTGCuIrImxgnKSp2MFV/UiIspAYmIi5syZg+nTp2Pz5s1ISUnB+PHj0bdvXwQHB3u6eERElAcwVhARUUY8FSvY8ENElAkHDx7EtGnTMGvWLMTGxqJdu3ZYtGiRp4tFRER5CGMFERFl5E7GCjb8EBFlQUpKCn799VdMnz6dF/NEROQQYwUREWXkTsQKNvwQEREREREREVkUJ3cmIiIiIiIiIrIoNvwQEREREREREVkUG36IiIiIiIiIiCyKDT9ERERERERERBbFhh8iIiIiIiIiIotiww8RERERERERkUWx4YeIiIiIiIiIyKLY8ENEREREREREZFFs+CEiIiIiIiIisig2/BARERERERERWRQbfoiIiIiIiIiILKpQVndMTU3FmTNnEBwcDJvNlpNlokwQEcTHx6N06dLw8rJ2Ox4/c2QFBek7C/B7m1cUpM8dP3NkBQXpOwvwe5tXFKTPHT9zZAWZ+c5mueHnzJkzKFu2bFZ3pxx28uRJlClTxtPFyFX8zJGVFITvLMDvbV5TED53/MyRlRSE7yzA721eUxA+d/zMkZW4853NcsNPcHCwdpKQkJCsHoayKS4uDmXLltX+HlbGz1zBsmMH0KJF1vZdvRqoUycnS5NzCtJ3Fsjb39uhQ4caHu/bt09LP/nkk4a8hIQELV2okDF0/vrrr1r6xRdfNOR16NDBrbKkpqYaHuf0L60F6XOXlz9zRO4qSN9ZgN/bvKIgfe74mbtzrHpNnxdk5jub5YaftC5xISEh/LLkAQWhiyI/cwVLUFD29s3rH5GC8J0F8vb31tfX1/BY36Dj7+9vyEtJSXH4PPPjgIAAQ567rzm3G37SFITPXV7+zBFlVkH4zgL83uY1BeFzx8/cnWP1a/q8wJ3vbJYbfoiIiPK6VatWGR5/+eWXWtrc8BMTE6OlBw0aZMjz9vbW0ubGnUaNGmnpn376yZC3aNEiLT127FhDXpEiRbS01edSICIiIiLP4ZUmEREREREREZFFseGHiIiIiIiIiMii2PBDRERERERERGRRnOOHiIjytYMHDxoef/TRR1r60KFDhrxatWpp6f379xvy9BM6FytWzJB36dIlLV2jRg1D3pUrV7S0eeJn/TxCr7zyiiGvUqVKWnrAgAGGvBIlSoCIiIiIKCewxw8RERERERERkUWx4YeIiIiIiIiIyKI41IuIiPKklJQULa1fTh0ApkyZoqU3btxoyAsMDNTSDRs2NOQFBQVp6Rs3bhjyDhw4oKX1w74A49ArfbkAYMuWLVq6X79+hrzw8HAtHRcXZ8g7e/asln7hhRcMeVOnTtXSJUuWNOSlpqZqaS4DT9lx4gSgG8XotmLFgHLlcr48RERElDvY8ENERERUwJw4AdxzD2Bq/3SLnx9w8CAbf4iIiPIL/lRIREREVMBcupS1Rh9A7ZeVnkJERETkGWz4ISIiIiIiIiKyKA71IiKiPMk8r4/e7t27tXSpUqWc7mdeXl2/9HqXLl0Mefv27dPS+vl3AODTTz/V0lFRUYa89u3bOy2zfh6hgIAAQ15ISIiW1s/bAwA//PCDln711VcNeZzXh4iIiIgyg1ePREREREREREQWxYYfIiIiIiIiIiKL4lAvIiLK8/RDtADjEKrixYs7fW5ycrIhLzg4WEtfvHjRkNeyZUstff78eUPeTz/9pKUrVKhgyKtataqWTkxMNOTdunVLSyclJRny9EvGm4ernTp1Skubl493NQSOiIiIiMiMPX6IiIiIiIiIiCyKDT9ERERERERERBbFhh8iIiIiIiIiIoviHD9ERJTnHTt2zGmefr4fALh586aWNs+HExQUpKVPnDhhyIuLi9PSERERhjz9vD7nzp0z5B0/flxL6+cQAoCSJUtqaZvNZsjTz90THx9vyNO/pqtXrxryihQpAiIiIiIid7HHDxERERERERGRRbHhh4iIiIiIiIjIojjUi4iI8rzTp08bHuuHQpmHXumXRtcP3wKA/fv3a+nY2FhD3tmzZ7W0fql183O3b99uyCtWrJiW1i/tDgAnT57U0uZl2RMSEhyW2ezAgQOGx02aNHH6XCIiIiIiM/b4ISIiIiIiIiKyKDb8EBERERERERFZFBt+iIiIiIiIiIgsyjJz/IiIwzQAeHllv31rzZo1hscPPPBAto+ZGYmJiVo6MDDwjp6biMjTzHP8+Pr6aml9/QgAycnJWrpo0aKGvH///VdLX7lyxZDn5+fn8PgAUKJECS1drVo1Q17hwoUdHgMwzkVUpUoVQ96yZcu0tH6ZecA4b9DevXsNeZzjh4go7zPfj+jnkStdurQhTx/Hxo8fb8gbOHCgljbfA/j4+Dg9v35eOW9vbzdKTERWxh4/REREREREREQWxYYfIiIiIiIiIiKLssxQL5vN5jCdkUGDBmnpEydOGPKaN2+upZcvX27Iq1ChgpYuW7as2+fTD0EoVMj52z9u3DjD459//llLr1ixQkvrlwMmIrIq/ZApwFj3HTlyxJB3/fp1LV2+fHlDnn7ol36IFgBcvnxZS5uHgV27dk1Lx8fHG/IiIyOdHlPfvf7q1auGvA0bNmjpGjVqGPLat2+vpc2vj4iIPEc/hMt8z3H06FEt/corrxjyBgwYoKW3bdtmyBs8eLCWnjNnjiFvyZIlWvqHH34w5D300ENaWj+UDAACAgK09PPPP2/I08dCV9NlEJF1sMcPEREREREREZFFseGHiIiIiIiIiMii2PBDRERERERERGRReXqOn9TUVMPjrM7jox9ve9999xnynn76aS1dr149Q55+bgbzksD//e9/tfSCBQvcLoureX1mzZqlpWfPnm3I089nceDAAYfbiYisKi4uzvBYv/StuR7Uz8FmzqtYsaKWNi/ZvnnzZi198eJFQ1716tWdHjMpKUlL6+cXAoxzLOjLBQDTpk3T0sOHDzfk6ecUMi9XT0REnuPqHkQ/59uiRYucPm/evHmGx+3atdPSe/fuNeTdvHlTS5vnFV29erWW9vPzc3o+V/cfRFQwsMcPEREREREREZFFseGHiIiIiIiIiMii7ki/P/PSgPoukq7yvLyct0vdunXL8PjcuXNaum7duoY8/XKKb775piGvVq1aWvr48eOGPH33+mrVqhnyli1bpqXDw8MNeW+//baW7tq1qyFPv9TvunXrDHlffvmlw+cBQO3atbX0XXfdpaXNywoTEVmRuX7WD9PSD8sFgB49emjpsWPHGvL0das5xuiHk+mXdgeACxcuaOmdO3ca8vRxxMfHx5CXnJyspc31tX6pef2QMMA4nIzL6xIR5Q8rVqzQ0tHR0Ya8cuXKaekZM2YY8vT3GeYpJAIDA7W0eZiZfgn3Zs2aGfL05//1118Nec8884yWTklJcZgmImthjx8iIiIiIiIiIotiww8RERERERERkUWx4YeIiIiIiIiIyKLuyBw/rpY9dJW3du1ap3kjR440PNbPe6NfIhcwLgt/6tQpQ55++V4z/bK85jkWOnfurKVDQ0MNeVOmTNHS06dPN+QFBwdr6UuXLhny9GN/GzdubMjbtGmTltbPQ8Hl3ImoINDPYwAAxYoV09KxsbGGPH3dXblyZUOefs6dAwcOGPL0c8eZ63X9HENnzpwx5DVt2tTpfv/++6+W1tf/AHD06FEtbZ7/R78sr7me1y/1bp4biIgoPxMRh/OauZofVH+db57zzRV9PEhKSjLkuTqOfi63Dz/80JCnr9fNy6uXKlVKS3/11VeGvPvuu09Lm+v11q1ba+kiRYoY8vTzhV68eNGQp583aO7cuYY8/Rw/+qXeuew7kXWxxw8RERERERERkUWx4YeIiIiIiIiIyKI83p/vyJEjhsf6Lvs//vijIU/fLX/EiBGGPP3S6/ql3c15+m6dgLEbpnkJQ33X0Rs3bhjybt68qaUff/xxQ16XLl209MGDBw15+qUVy5Yta8hr27atljYPF5gzZ46W1ncxNS/7TkRkFfqhV/o0YFyK3dwtXv/YPExKH2Puvvtup3n65dvNx6lbt64hTx8fzOfTn0M/TBcAgoKCtLS5+75+KLB+eABgjHGRkZEgIrIKm83mchqItOc442iYmLP9sjrESb8Uu34YMADUrFlTS+vvPwCgaNGiWjoiIsKQp5+K4qWXXjLknT9/XktXrVrVkKe/dwgJCTHk9e3bV0ubY9p3332npfXDvojIutjjh4iIiIiIiIjIotjwQ0RERERERERkUWz4ISIiIiIiIiKyqGzP8RMdHY2goCDMnj3bsL1EiRJa2jw/jn7ZWvPyifp5Dlq1amXI0y91aF6GXT/fg3mMq35JRvM8PjExMVraPDeQvpz65YEB4xw/5jz9vA333HOPIa9Zs2ZaOjw83GlZFixYYMjTjwveu3evltYv60tEZCX6OeB8fX0NefrYcfXqVUOefu4E87wN+nne/P39DXn641y+fNmQp49Hhw4dMuTp5+Mx0883ZF4eWF8W81Lv+sfmspiXficisgpny7m7kpkl3PX0dfDUqVMNedu3b9fSxYoVM+T17t1bS+uXWgeAH374QUvv27fPkKePW02aNHFari+++MLw+NVXX3VYLsB4z9O0aVNDXrly5RymAeCff/5xen4isib2+CEiIiIiIiIisig2/BARERERERERWVS2h3p9/fXX8PX1xc6dOw3bzd3yDSfVdb03L1t+8eJFLW3uvq8fPhYYGGjIO3bsmJbes2ePIU+/RKJ+uV7AOEzLPCTNPCxMT//6zMMFGjRooKW3bNliyPv888+1tH54GgDce++9Wtq85KT+uZUqVdLS5qWDiYisQl9fuxrqVatWLUOefvlzff0PGIfiulrq3VwH6495+PBhp2UxD1HQL+GuH1YAAMWLF9fS5nigj036MgPpYyMRkVW4s5y7mf563TzsSz91gjke6IdNmeNBr169tPTq1asNedWqVdPSR48eNeTp72PM9xX6+xhXzK9fvxS7+d5EP+WDfpl5AGjfvr2WNscR/dCvEydOaGkOJSayLvb4ISIiIiIiIiKyKDb8EBERERERERFZFBt+iIiIiIiIiIgsKttz/HTt2hWBgYGGZdgB4OTJk1r6ypUrhjz9+NEzZ84Y8vRz/hw/ftxpnn5OHwBITEzU0uble/VzJ5jnFNIvtVuzZk1Dnn75XvNyuvPmzdPSf/75J9ylf+2ulmI3z2Hk4+OjpfXjkP38/Nw+NxFRfqKf18A8P45+bjX9/DuAcc4d8xwLJUuW1NI3b9405Oljh/55ALBixQotbV6iNzIyUkuHh4cb8vTnN88Hp5+LQl/HA8Y5HvSvBzDO/0NEZEWulnRPTU01PHa1nPuOHTu0tDkeFC5cWEu/8cYbhry6detqafM1+f79+7W0fq42wDhvkPk1fPfdd1p6wIABTstspo8B//77ryGvSpUqWto83938+fO19LPPPmvIq1OnjpbevXu3ltbfTxGRtbDHDxERERERERGRRbHhh4iIiIiIiIjIorI91Ktq1aoICQnB3XffbdgeERHhdB/9UoTmbpf6ZRH13fwB4Pfff9fSvXv3NuTpuzcWLVrUkGfuQp8THn74YS29dOlSQ17t2rW1tHmJXn13VPPSivouoeaulmfPntXS+iFiXM6diKzq0qVLWjo4ONiQp+/6XqFCBUOevhu+uY7UD+8yDxHTD1E2D6fSD2c2D9nSDwMw5+mHqJmXpNczD+fSP9c8XMAcN4mIrEJEICLpli3XM0/pEBcXp6Wjo6MNefqhUeYpHfTDdt98801D3k8//eTw+ABQtmxZLW2+51i5cqWWvu+++wx5+vsa/fBhAGjdujWc0d9XnD9/3pD3xBNPaGn9vQkAdOzYUUs//fTThjz91BP6mGIeAk1E1sEeP0REREREREREFsWGHyIiIiIiIiIii2LDDxERERERERGRRWV7jp/Q0FCEhISkG/+6fPlyLW1ehle/fGJYWJghr0aNGlravNz5wIEDtbR++VwAuHXrlpbWzwsBGMexmunncTDP6aCfV8E8nviuu+7S0uZ5G9auXaul9eNyAeM8DublJ/Xjmc2vXT9PhXk8MRGRFenrZD8/P6d5xYoVM+Tp51EIDQ015OnnVouNjTXk6et58zxr+vmAYmJiDHn6+RHOnTtnyNPHOFexyBwn9Y/N8Ucf74iIrMRms8Fms6Wr91zRX68vXLjQkHfw4EEtbZ4fTb/U+549ewx5ly9f1tIXL1405C1atEhLv/LKK4a8VatWaenRo0cb8vTxYcyYMYY8/Rw/V69eNeSVKFECzphjjp65bHr6Zef186Ry7lAi62KPHyIiIiIiIiIii2LDDxERERERERGRRWV7qFca/dKGjh7rHTlyREubh1cdPnxYS5u74euXRtd33QSMyw+GhIQY8vRDy2w2myFP3+1fv1wvYByWZV5OV9+1snjx4k7Pl5qaasjTH+fKlStwxrx0sb5sFStW1NLmIXZERFZkXu5cPxTKPExq7969WlofN8yPzTFGHx/Cw8Odnl9fxwPGJdzNQ3/1Q3rNQ7T0ccQ8fFnPPOTBPBSYiMgqNm/ejMDAQEydOtWwXT8EyVwn6ut1c57+elo/DBgwDsU9e/asIW/jxo1a+vfffzfkuVryXD8s2DxkS08/lAwA7r//fi1tvjdq166dljbHptmzZ2vpwYMHG/IqV66spevVq2fI0y9zP3HiRC2tn3aCiKyFPX6IiIiIiIiIiCyKDT9ERERERERERBbFhh8iIiIiIiIiIovKsTl+MqNSpUpuPa9mzZq5XJL8zzxnERGRVejrN/O8Ovr52fbv32/Ia9KkiZauWrWqIU8/r45+/h3AuGSveZ4I/bwH5jkQ9PP/mOd00M/r5uPjY8jTzwFnnsNIXzbzUvbmOY2IiKzi3nvvRUhICPr372/Yrq+fzXNk6ufEMc+Po1/C3Zynr2ffeecdQ56+fjbPHVq0aFEtrV8WHTDOG/Taa68Z8vRzgurnAgKMseP999835J06dUpLR0REGPL0Mcacp59/LjAw0JCnj42u5swjIutgjx8iIiIiIiIiIotiww8RERERERERkUV5ZKgXERFRRvRd2M1Dr/TDwIoUKWLIe/HFF7X00aNHDXnbtm3T0vpu9wCwe/duLb1v3z5Dnv4c5qFe+i775iFpZ86c0dI9e/Y05DVq1EhLm4eI6ctiZl6inojIKgIDAxEYGIjmzZt7uigeY14+/k6Ki4vz2LmJKHfx6pGIiIiIiIiIyKLY8ENEREREREREZFFs+CEiIiIiIiIisijO8UNERHmSfh4fM/08O82aNXP6vMjISJeP9Vq0aOE0T7+0782bNw15+iVzs8o835Cr164vCxERERFRRtjjh4iIiIiIiIjIotjwQ0RERERERERkURzqRUREeZKvr6+WdjX0ybyEup556XVvb28trV8uPqNz6JdQz+rQLlfnCw4OdlpO89CuW7duZen8RERERFQwsccPEREREREREZFFseGHiIiIiIiIiMii2PBDRERERERERGRRnOOHiIjypEuXLmnppKQkQ55+DpxChbIWysxz+ujn4HE1309Wmefq0b8G8xw/+iXjzXmu5jQiIiIiIjJjjx8iIiIiIiIiIotiww8RERERERERkUVxqBcREeVJ+qXYzcObkpOTtXRERESOnM/d4V2ulmV3ledqqJd5iXj90Db9awXSD/0iIiIiInKFPX6IiIiIiIiIiCyKPX6IiIiI7rBhw4Zlab+xY8fmcEmIiIjI6tjjh4iIiIiIiIjIotjjh4iI8iQvL/tvE/Hx8Ya82NhYLa2fC8jM1bw6WeVqLqCsLgNvXpLe1fxGgYGBWToHERERERVM7PFDRERERERERGRRbPghIiIiIiIiIrIoDvUiIqI8qU+fPlp669athjz9UK/69es7PYZ5CJUn6YeumZmXpNc/Nr+GsLCwHC0XEREREVlb3rkiJiIiIqIChaubERER5T4O9SIiIiIiIiIisqgs9/gREQBAXFxcjhWGMi/t/U/7e1gZP3MFS0JC9vbNqx+TgvSdBbL3vdWv5HXt2jVD3vXr17W0+dj6lbvM73NWV93KCebVx/TlNK9advPmTS1tLnNiYqKWdvd9LUifu/wSK/R/48zIqdeVnTo2bf+cKIqn34e8qiB9Z4H88721uoL0ueNn7s6x6jV9XpCZ76xNsvjNPnXqFMqWLZuVXSkXnDx5EmXKlPF0MXIVP3NkJQXhOwvwe5vXFITPHT9zZCUF4TsL8Hub1xSEzx0/c2Ql7nxns9zwk5qaijNnziA4ONijv6AWdCKC+Ph4lC5d2uXEoVaQnc9cXFwcypYti5MnTyIkJCRL58/uMaxQBiu8Bk+XoSB9ZwHGiryiIH3uGCs8XwYrvAZPl6EgfWcBxoq8oiB97hgrPF8GK7wGT5chM9/ZLA/18vLysnxLcH4RGhrq6SLcETnxmQsJCcnylzqnjmGFMljhNXiyDAXlOwswVuQlBeVzx1iRd8pghdfgyTIUlO8swFiRlxSUzx1jRd4pgxVegyfL4O531tpNuUREREREREREBRgbfoiIiIiIiIiILIoNP1Qg+Pr6YuTIkfD19fXYMaxQBiu8hrxSBiLKe6xSv3i6DFZ4DXmlDESU91ilfvF0GazwGvJKGdyR5cmdiYiIiIiIiIgob2OPHyIiIiIiIiIii2LDDxERERERERGRRbHhh4iIiIiIiIjIotjwQ0RERERERERkUWz4IXIgKSkJffv2xbFjxzxdFMoDTpw4AUfz4IsITpw44YESEVFewFhBeowVROQIYwXpeSpWZKnh5/hxwGYDduzI2cJYxfLlQLVqQEqKp0uSXvnywIQJOXvMW7fUcf/5J2ePm10HDx7EwIED0aZNG7Rp0wYDBw7EwYMH3dq3cOHCmDt3bi6XsOBYs2YNkpOT021PTk7GmjVrXO6bnJyMqKgonDp1KreKl6EKFSrg4sWL6bbHxMSgQoUKHigR5TeXLwMlSqj4mVc0agSwmmOsyEsYK0ivd2+ga1f3n2/V+5O8dF8xYwYQFpa5ffT3Hnn1nsEdjBV5B2NF1uT5Hj/z5gHt2wNFizqvzG/cAF5+WT0nKAh47DHg/Hnjc06cADp3BgIC1MX3G28A+s/L9u1A3bpq/4cfBmJi7HnJyUD9+sDmze6VeehQ4J13AG9v9TglBRg7FqhaFfD3B4oUAe6/H/j660y9FXmWjw/w+uvAm296uiR2c+fORY0aNbB161bUrl0btWvXxrZt21CjRg23K96uXbtiwYIFWS5DXmnd9/b2xoULF9Jtv3z5MrzTPqQmu3btcvufO1q1aoUY/ZfqtqtXr6JVq1Yu9y1UqBDGjRvnsIK/U0QENpst3faEhAT4+fl5oER5z7lzwH//C0RGAr6+QNmyqi5dvjxnz+Nu4/W5c8CzzwKlSgGBgUC9eukbObZtA9q1UxexRYsCzz8PJCTY82Ni1GsIClLxYft24/4vvwx8+ql75X7/feCRR1T59ebOBVq2BEJD1Xlq1QKioowxKLucXai/8w4wbBiQmppz58pvGCvsGCuyz4qx4uJF4MUXgXLlVN1eqhTw4IPA+vWeLlnW8L7Cs/LiPYM7GCvsGCuyz1OxolCuHTmTkpKAwoXTb09MBJo1A7p3B557zvG+r74KLFkC/PyzungeOBDo1s0elFJSVOVcqhTw99/A2bNAz57qfB98oJ7Tvz/QujUwZ45Kf/AB8MknKu/TT4GmTYGGDTN+HevWAdHRKkikGT0a+Oor4PPPgQYNgLg41dJ95Yr7709edeuWqsR79ABeew3Yuxe4915PlwoYOnQo3nrrLURFRRm2jxw5EkOHDsVj+j+QE5UrV0ZUVBTWr1+P+vXrIzAw0JA/aNAgl/unte6PGDEi8y/Ahbi4OKxYsQL33HMPqlWrluHzHXUlBICbN2/Cx8fHYV6dOnVgs9mc7puWZ7PZkOLGT1DOKrjLly+ne18dad26NVavXo3y5rvmXDZkyBAA6vWOGDECAQEBWl5KSgo2bdqEOnXq3NEy5UXHj6s6MiwMGDcOqFlT1el//KEung8cuPNl6tkTiI0FFi0CihUDfvhBxZF//lEX42fOAG3bAk88oermuDjglVfUL8y//KKO8f77QHy8aiCaMkXFoLRfKTduBDZtAiZNyrgs164B06ap90Nv+HDgo49UDPvgA6B0aeDwYWDqVGDWLGDw4Bx8Qxzo2FHFu99/VzGyIGKssGOsyDorx4rHHlPXejNnqob98+dVg/7ly54uWdbwvsLz8to9gzsYK+wYK7LO07GiUMmSquX7s8/UxTEALF0KvPcesGePal1u3BiYOBGoWNHxQVJSVOX599/An3+qXwUWLlQV07596mK2Vy91kVvodlOTzQZ8+aW64Fy+XLWUjxqV/tjPPqv+d9Y9/upVdUH9ww+qggWAb75RXSI3blRd2f/8U5Vj2TKgZEmgTh1gzBjV2jxqlGq42L8f+P57oEoV4KmngMWL1bGOHlXH37rVvTd09mz1C7K+sW7RIuCll4DHH7dvq13buF/LluqXXj8/1WLv4wMMGGB8T2JjVSv5woXAzZuqsv/sM/uxoqOBIUPU605MVO/Bhx+qmxtnvv5aHXPuXKBNG/U3f+MNYO1a9Su5+bPRsiVQo4b6O373nbrBW7kSCA9XQWz2bPXeetrZs2fRs2fPdNufeeYZjBs3zq1jTJs2DWFhYdi6dSu2mj4ANpstwwoasLfuv/rqq+4V3IHu3bvjgQcewMCBA3H9+nU0aNAAx48fh4hg9uzZToPNpNt3pDabDV9//TWCgoK0vJSUFKxZswZVq1Z1uG9O/ZrQrVs3rQy9e/eGr6+voQy7du1CkyZNMjxOx44dMWzYMOzevdthsOzSpUuGx6hbt67DIGGz2eDn54dKlSqhd+/ehl8Ktt/u4iEi2L17tyGg+fj4oHbt2nj99dczPLfVvfSSqtM3b1b1Rpp77wX69rU/PnFC9Qpavhzw8gI6dAAmT1b1MpBxHdayJfDvv+qiPO0r5eQaAn//rRpr0i6s33lH1WVbt6qGn8WL1UX6F1+osgCqwaVWLeDIEaBSJRUXnnxSxYXnnwf+9z/1vKQkVT9//bX9F1hXfvtN/VLeqJF92+bN6kZgwgRjA0/58iqGxMbat02Zom4YTp4EKlRQryUtNgLA+PEq7h09qn75ffhh4OOP1S/Nq1YBffqo56V9/EeOVLHF2xvo1EnV2wW14YexgrHCjLHCLjZWXQ+uWgW0aKG23X13+gYLV3UQoHodvvKKagR55RVVlzVrpvaJiFDPSUlR15/Tp6u6qV+/9PV7Zu9PHCko9xUZvVfHj6t4MneuisObNgGVK6s42Lix/TgzZgDvvgtcuqR6ejVrZjxPVu498to9gzsYKxgrzPJjrCi0dKmqqLp3B1asUBsTE9WXuFYt1e393XeBRx9V3SHTLpDT3LypKrTjx1VwKF5c/d+zp/oltHlzVSk8/7x6/siR9n1HjVJdFSdMsDcIZdbWreoiXF/BVK2qGp82bFAV9IYNqoEi7eYCUJXXiy+q1ua6dVWF+ddf6mJ/+XL12gF1cf/xx0BwsHvlWbsWePpp47ZSpdR7+9JL6v1xZuZM9b5v2qTK3Lu3qhjbtVP5jz+uunT+/rv6BeKrr1RjzaFDKtAmJKiL+PffVzcZ336rgu/Bg+r9MPv4Y/Xvzz9VEI+NVUGuf391g3T9evrPRlo5X3wxfTffhg3V688LWrZsibVr16JSpUqG7evWrUPz5s3dOkZOVFLZbd0H1DjW4cOHAwDmz58PEUFsbCxmzpyJ9957z2kF/dlnnwFQlcvUqVMN3S99fHxQvnx5TJ061eG+d999t1uvLyOhoaFaGYKDg+Hv728oQ6NGjfCcs5/cdF566SUAwPjx49PlufvrQIcOHTBlyhTUrFkTDW9ftW7ZsgW7du1C7969sW/fPrRt2xbz5s3DI488AgBYuXIlAKBPnz6YOHEiQkJCMjxPQRMToy4w33/f2OiTJm2IUWqqGuoUFASsXq26ur/8supxs2qVek5Gddi8eaqufv5557/UpmnSRN1kdO6syvDTT6r7fsuWKv/mTXVxro9paR/PdetULKhdW9V9/fur3jppceHjj9VxGjRw7z1au1Z169f7/nv1Xtz+aKeT9r7Nn68ahiZMUHFu8WLVkFOmDJB2LeHlpeJthQrqpuKll9TQgC+/VO/DhAkqjqdNRaC7VkPDhioOF1SMFYwVZowVdkFB6t+CBep6WnePZeCqDkpz7ZpqwJ41Sz3/mWfUD4/ff6/yP/1UNTJMn64aDz79VNV/aY0vQObuT7LKKvcV7r5Xw4erv0vlyir91FPqx49ChdT9SL9+qiGna1cV6/X3cUDm7z3S5KV7BncwVjBWmOXLWCEicvKkCCBy8KA4dPGiyt+9Wz0+dkw9XrtWpE0bkWbNRGJj7c9v00bkgw+Mx5g1SyQiwv4YEHnlFcfncyTtnNu3G7d//72Ij0/65993n8jQoSr93HMi7dsb8xMT1fF++0093rNH5IEHRMqVE3nqKZGrV0W+/VbkkUdETp1S+1esKDJ8uOtyhoaq/fT27hWpVk3Ey0ukZk2RF16wnzdNixbqfTS/hjffVOm1a0VCQkRu3DA+p2JFka++cl6ee+8VmTzZ/vjuu0U++0y9NxER6nWnGTMm/ftk/my0aCFSt67jc02cKFK+vPOy3ElTpkyR4sWLy8svvyyzZs2SWbNmycsvvywlSpSQKVOmyMKFC7V/Gbl586YcOHBAkpKSMl2O8uXLO/1XoUIFt47h5+cnJ06cEBGRZ599Vt68/aH4999/JTAwMMP9W7ZsKTExMZkuu9nevXvl999/N7x37rx/qamp0rt3b4mPj892GbKjf//+EhUVlW77mDFjpH///iIi8u6770r9+vVdHufkyZNy8uTJXCljfrRpk6oj5s1z/bw//xTx9ha5/VEWEVU3AiKbNzvfz1kdlpErV1R9BogUKqTqzz/+sOfv2aO2f/yxyM2bIjExIo89pp6fFr9iY1U8KFdOxYe9e0UOHRKpXFnk0iVVl1eoIPL448YYaPbIIyJ9+xq3dewoUqtWxq+jSRMVw/Qef1ykUyfn+/z8s0jRovbH33yjYpMjCxeq2JSSknFZrIixwo6xQmGsMPrlF5HwcBE/P1UfvfWWyM6drvdxVAcBIkeO2Ld98YVIyZL2xxERqj5Ok5QkUqaMqj+dcXZ/Yr5XcMTq9xVmzt6rr782HhcQ2b9fPX7qqfSx5oknnMeTNO7E7bx0z+AOxgo7xgolP8YKBAaKBAYaK6tDh0SefFJd0AYH2/OXLFH5aZVFmTIijRqJXLtmPGixYipApB07MFA9BlTFKKLS333nfkFzu4I2u3RJvf6TJ0UefVRk1CiRhARV0S5a5LycPj4iP/2UfntKirq5+ewzdTxvb5F+/ez5LVqIvPSScZ8uXUT69FHpzz9XFbz+PQ0MVNvSXmd8vMhrr4lUraoq5bT8N96wH/Puu9XfLTxcJDraeL7//EekcOH059C/Ty1aiNz+LKfzv/+JlCjh/L25k2w2m1v/vLy8nB4jMTFR+vbtK97e3uLt7S3Rt9+wgQMHyocffninXopUrlxZ5syZIwkJCVK8eHFZvny5iIjs2LFDiuqvrHJJdHS01KpVS3u/9O+dq/cvTUpKihQuXFgOHTqUI+W5fv16lvYLCQmRw4cPp9t++PBhCQkJERGR/fv3S1BQULrnpKSkyOjRoyUkJER73aGhoRIVFSUpBfWO+baNG91r+HF2kRcWJjJzpkq7W4e50/AzcKBIw4Yiy5aJ7Nih6vDQUJFdu+zP+f57dePh7a3q7tdfV4/HjnV+3FatRBYsUK+nXTuRW7dEevUSGTLE+T7t26ev3zt0cK/hJzxcZMYM47YJE1R8SvPXXyKtW4uULi0SFJQ+3rpq+PnzT/VccxwvKBgrcg5jhXVjxfXrqq6IihJp3FjVmd98Y893pw4KCDAec948EZtNpWNj1fNXrzY+p2tXY8OPu/cneaHhx+xO31e4+17pf3iJiTH+HerUERk92njeCROM8SSrcTsv3TO4g7Ei5zBWeC5WeO3Yobr9HT4MPPCA6gWUNvv8//2f6ua3aZPafuuWsbdQp07Arl2qy6NeQoKa3yft2Dt2ALt3q3Pox6i6MfdShkqVUuXSz4cAqMnnSpWyP8c8G3/a47TnmA0ZosYhlymjhiE8/rgqb+fO9mEJjhQr5nhyNS8v4L771DHnzVPdWadNA/S9/syTW9ts9tVWEhLUOGj9e7pjh+pK+cYb6jmvv666xX7wgeo+uWOH6opq/rs1b67GUv/0k3F7QoL625vPof9sAM7/bjExroey3Umpqalu/XPVle+tt97Czp07sWrVKsMM623btsWcOXMyVZ5bt27h4MGDWZpB/pVXXkGPHj1QpkwZlC5dGi1vj1VZs2YNatas6dYxTp06hS+//BLDhg3DkCFDDP8yMnjwYFSoUAEXLlxAQEAA9u7dizVr1qBBgwZY5erLcJuXlxcqV66My9mYCTIlJQVjxozBXXfdhaCgIBw9ehQAMGLECEybNs2tY/j5+eHvv/9Ot/3vv//W/r6pqakOZ9MfPnw4Pv/8c4wdOxbbt2/H9u3b8cEHH2Dy5Mk5PsleflO5sqqrcmICZ3frsIxER6tJL6dPV8Nha9dW3dMbNFBz+qR5+mm1+tfp02qi0lGj1Ao2kZGOj/vNN2oI1iOPqDjQtauqtx9/PPNxoUoVNSQiKSlzr83s+HHgoYdUd/65c9UwhbTX6M77FhOj6nRdb+kChbHCiLGCscIRPz817cCIEWr+tN697UN+3K2DHF3jOpujzRl370+ywyr3Fe6+V/q/S9p0JZlZ6TGrcTsv3TO4g7HCiLEin8YKc0vQpUuqtXfNGvu2tWvVtvnz1WN9K/mkSap1d9Uq+/ObNEnfrd1Mfzx3OGuZj41VvVR++cW+7cAB9dwNG9Tj335Trc/nz9uf89VXjodOiahfiO+7z971PSRE/WIsIjJ4sPrnTOfOrvPTbN1q7HLZokX6/R55RP2SLGIfJnHsmPNj1qihfo1JEx+vWt/1x01rdV+/Xv0CMG6cPe/tt0XuuUd1r3XGUTnTPPOM+mcV5cqVkw23P0RBQUFay/zhw4clODjYrWPkVOv+li1bZN68eYZujYsXL5Z169ZluO+yZcskICBAatSoIYUKFZI6depIWFiYhIaGSqtWrTLcv2jRorLzdr/ukJAQOXDggIiILF++XOrUqeNW+RctWiTNmjWT3Wkf+EwaPXq0REZGynfffSf+/v7a+zh79mxp1KiRW8cYM2aM+Pv7y6BBg7RuuoMGDZKAgAB57733RERk/Pjx0rZt23T7RkREOOx+umDBAildunSWXpOVdOggctdd6tdLsytX1P+uhnpt2aIeu1OHVa4s8sknrsuza5c67r59xu3t26cfNqU3bZr6VTqtzHoXLqgeS6dOqcdduqhfPkVULKtd2/lxx41Ln5/WUyrtGGZpZXA21KtzZ5X+5RcVA/U/EI0Zo46ddozvv1e/wjvyzjvphxlT5jBWKIwVBSdWfPqpfSiXO3WQo16H8+er56RxNNSrbFl7j5/M3p9kxMr3FVl9r65cUdtWrlSPHQ31evJJ498yM/ceela7Z3AHY4XCWOG5WIEjR0SWLhXp3VskOVlVSkWLqi/j4cMiy5eryspVZfHZZ+qicu1a9XjpUjV3wqhRaozrvn0iP/5oHMfqbsPP5cvqPEuWqH1mz1aPz561P2fAADWGdsUKkX/+Ud1QGze25ycnq4qpfXtV0S5dKlK8uBqnbHb9uuquqK8IO3ZUF947dqhhUo66XKaZNEnEPJTvscdExo9XF/rHj6sKtVEjkSpV7I0sGTX8pKaqi/PatdU8FceOqcabt9+23zQ9+qjqlrl9uyrrww+rxh1nle/atervlvb49Gn1vvznP6rrp/mz4ayc+mObxyF70qpVq+Shhx6SihUrSsWKFeXhhx+WNfoomAF9RaCvoHfs2KF14cvIoEGDpH79+rJ27VoJDAzUjrFgwQK3Kzez5ORk2b59u9vja++77z559913RcT+OuLj46VLly7y5ZdfZrh/WFiYHD16VEREIiMjZcWKFSIicuTIEfH393erDGFhYeLj4yNeXl7i5+cn4eHhhn8ZqVixoixbtszwGkRUF8qwsDC3yiAi8t1330mjRo208zZq1Ei+//57Lf/atWsOu3z6+vrKQQeToB04cED8/PzcPr9VRUeLlColUr26ulg+dEjV+xMnqvpURNVhdeqING+uLlA3bVJ1ZYsW9uO4U4e1a6caXU6dUnMWOHLrlkilSupcmzapuuyTT9SwgrRu7iJqDoKtW9UcZp9/LuLvr8rsyNNPG+cs+OgjVf59+1SMMA/l0tu1S8VE81d26FDVGPbGGyJ//63iw7Jlqg7WNyoVLizy5Zfqff30U7VP2oX5jh32BqToaFUH33WX8aZr/Xr1eNky9Z6lDb8QUe+/gyHqBQpjhcJYYcdYoVy6pIa3zpql5vU5elRdA5csaf+B1506yJ2Gn7FjRYoUUdv371fX3cHB9oafrNyfOFIQ7iuy+l6ZG342bFANXOPGqfgzebIanq3/W2b23kO/LS/dM7iDsUJhrLDLb7EC/v6qQnrlFXVhLqLG6larJuLrq+YgWLUq48ri00/VF339evV46VL1S6W/v2rZbthQjefUTuxmw0/ahHDmfyNH2p9z/bq66A4PV7/WPvqosQIXURVjx46qPMWKqfGojnq2DBum8vQOH1YVZkiIyIsvup4E8/JlNbb5duOliKjX3aqVCgo+PiqY9O6typQmo4YfEZG4OJH//leNoS5cWP0S0qOH/Rf0Y8fUefz9Vd7nn6c/rrnyXb1a9diaNEk9PnRIvX9hYeo45s+Gs4afv/9W++SVeSJmzZolhQoVku7du8vEiRNl4sSJ0r17dylcuLDhC+lK8+bNZdLtNyYoKEirpAYOHCgPPvigW8fIidb9wYMHy9e3Z99LTk6Wpk2bis1mk8DAQFmZFp1dCAoKkiO3Z1QMCwuTPbdn9N6xY4fcfffdGe7frFkzmX/7y/rUU09Jhw4dZN26ddKzZ0+599573XoNM2bMcPkvI35+fnL89hdG/z7u3bvXrYnosqthw4by3//+N932gQMHyv3335/r588PzpwRefllVcf4+KgL/y5d7BeQIiL//qu2BQaqePH44yLnztnz3anDNmxQccnX13jTYHbokEi3bmoOgYAAtY/5IvPZZ9WNho+P4/w0S5eqGKav+xMTVfmDg9WCBvpffh1p2FBk6tT02+fMUROAps3BUKuWaojR9zr68kuRyEhV71epkr6c48erX8v9/UUefFDl62+6RNSNTNGixvh56pQ6pgXmn80yxgo7xorss1qsuHFDXRfXq6du9gMCVM/wd94xXu9lVAe50/CTlKTq+pAQdT05ZIhIz57GOX6ycn9iVlDuK7LyXpkbfkRUT9gyZdTrfPhh9SOK/m+ZlXuPvHbP4A7GCjvGiuzzVKxwcdlMWfX66yLPP+/pUtxZ3buLvP++p0thV7VqVRk/fny67Z9++qlUTeuCkIG1a9dKUFCQDBgwQPz8/GTw4MHSrl07CQwMlH/++cetY+RE6/5dd90lW25365o/f76ULl1aDh48KO+88440adIkw/1Lliwp+26PealWrZrWtXDHjh1uVW5Lly6VuXPniogKLPfcc4/YbDYpVqyYNiFcbqtXr57MmjVLRIzv4+jRo6XZHRinsmrVKgkMDJRq1apJ3759pW/fvlKtWjUJCgrK1K89VHAtXqwuwvPS/K5Dh7oe+lYQMFbYMVZkH2MF5Qar3VfktXsGdzBW2DFWZJ+nYgUbfnLBlSuqQstLF/i56eZNNZ47L7Xc+/j4OJ1p3dfX1+3jHDlyRPr37y/33XefVKtWTXr06CG79MsCZSAnWvd9fX21Zf6ee+45GXz7Z5SjR4+61br/yCOPyP9ud7d77bXXpFKlSvLee+9JvXr1pE2bNm6/Fr3Lly9Lalo3MCeuXr1qSLv6l5EFCxZIaGiojB07VgICAmTcuHHSv39/8fHxkT///NOtMicnJ8u4cePkvvvuk5IlS2a6W+jp06fl7bfflm7dukm3bt1k+PDhcvr0abfOTSSifvHUz3HkaZ98YuxxVRAxVtgxViiMFZTXWOm+Ii/eM7iDscKOsULJj7GCDT9kSRUrVpSpDsZVTJkyRSpVqnTHypETrfvlypWTP/74Q5KTk6Vs2bKyePFiERHZs2ePW+NQo6OjtUnUEhIS5IUXXpCaNWtKt27dtG6OucHLy0vO3x7/ol+mUf8vo6Uv9dasWSNt27aV4sWLi7+/vzRt2lT++OMPt8szYsQIiYiIkE8++UT8/PxkzJgx0q9fPylatKhMdDaxCxFZGmOFHWOFwlhBRGaMFXaMFUp+jBU2kcwupkiU902ZMgWvvPIK+vbtiyZNmgAA1q9fjxkzZmDixIl44YUXMjzGtm3bULhwYW1pw4ULF+Kbb75B9erVMWrUKPj4+LhVlujoaIwdOxY7d+5EQkIC6tWrhzfffNPtJRNHjRqFCRMmICIiAteuXcOhQ4fg6+uL6dOn4//+7/+wYcMGt46TGd26dXP7ufPmzXO4ffXq1WjatCkKFSqE1atXuzxGixYtXOafOnUKZcqUcZi3ceNGNGrUKMNyVqxYEZMmTULnzp0RHByMHTt2aNs2btyIH374weX+sbGxmDZtGvbv3w8AuPfee9G3b1+EhoZmeG4iypsYK7KHsSI9xgoi62GsyB7GivQ8ESvY8EOWNX/+/7d33+FRVPsbwN9N70UgoYUSioAUBQQUkN6boBflglT1hxdEL4oXRYUEUbAgRRS9AgLqBQsdBaUXQZDeS6hSpIQQEkpC8v39ccxkZpPdbJJdNpm8n+fJw+ycnZmzy+68u2fPObMQH330kfaGql69OkaMGIFu3bo5tP3DDz+MkSNH4oknnsCJEydQo0YN9OjRA9u3b0enTp0wadIkF9be6IcffsDZs2fxj3/8QztRzZ49G2FhYQ4/nh07dhhOLg899JDN+w4YMEBbFhEsXLgQoaGhqF+/vravhIQE9OjRA7Nmzcrrw3JYjRo1sGnTJtx3332G9Zs3b0anTp2QkJCQ4z4CAwNx6NAhlCtXDqVKlcLy5ctRt25dnDhxAg899BCuX79uc9s//vgD7dq1g7+/Pxo0aAAA2L59O27duoVffvkFdevWzdfjIyL3YVYYMSuYFUSUFbPCiFlRCLPCjb2NiAq0kJAQbdb68ePHS9u2bUVEZNOmTVK2bFmH9tGqVSuZNWuWQ+NNHZHdJQFz8tdff0mLFi3EYrFo404tFou0bNlSLl26lOP2r732mjz77LNy9+5dbd3du3fl+eefl1dffdXhely7dk1Wrlwpc+fOldmzZxv+cjJgwACpV6+eJCYmauvWr18vISEh2U62l52qVavK1q1bRUSkcePG8t5774mIyLx586REiRJ2t23SpIn0799fUnWX7EhNTZV+/fpJ06ZNHTo+EZkTs0JhVjAriMg2ZoXCrHBfVrDhh0ypYsWKcuXKlSzrr127JhUrVnRoH8HBwXL06FEREWndurVMmjRJREROnz4tfn5+Du1j2LBhUrJkSfH395cnn3xSFi1aJCkpKQ4+CuXu3bsSGxsrpUuXFk9PT23m+TfffFO7HKM9PXv2lPr162sz8IuoyxXWr19fnn766Ry3L168uBzWX0f0b4cPH5b77rvPocewZMkSCQ4OFovFIqGhoRIWFqb9OTIBWlpamnTv3l2aNWsmt2/fljVr1khQUJD2f+KI//znPzLu78tIzJs3T7y8vKRy5cri4+Mj//nPf+xu6+fnJ4cOHcqy/sCBA+Lv7+9wHYioYGFWZGJWKMwKIrLGrMjErFAKY1aw4YdMyWKxaBOA6V28eFF8fHwc2keLFi2kb9++MmfOHPH29tZm81+3bp2UL1/e4bqkpaXJypUrpV+/fhISEiLh4eHy3HPPybp16xzaPiYmRqKjo+Xrr782XMZx3rx50qhRoxy3DwkJkW3btmVZ//vvv0toaGiO24eFhcmiRYuyrF+0aJFDk8CJiFSpUkVeeuklSU5Oduj+2blz5460bt1aHn30UQkKCpKpU6fmeV8iIlu2bJGPPvpIlixZkuN9IyIisp3wbcWKFRIREZGvehCR+zArMjErssesICJmRSZmRfYKQ1Z4uWYAGZF7LFmyRFteuXKlYYKstLQ0rF69GhUqVHBoX5MmTULv3r2xaNEijBo1CpUrVwagxsVmTOzmCA8PD7Rt2xZt27bF9OnTsXTpUowbNw4zZsxAWlpajtvPmTMHX3zxBVq1aoXBgwdr6+vUqYPDhw/nuH16ejq8vb2zrPf29kZ6enqO2w8YMACDBg1CXFycNg71999/x/jx4w1jdu05d+4chg0bhoCAAIfuDwB79+7Nsm7MmDHo1asX+vTpg8cee0y7T+3atXPc39WrV1GsWDEAwNmzZ/HTTz/h1q1b2vhie5566ikMGjQIH374oWFSvxEjRqBXr14OPyYiKhiYFVkxKxRmBRFlYFZkxaxQCmVWuKxJicgNLBaLdim/jOWMPx8fH6lataosXbo0X8e4detWrrtViohcuHBBPv74Y6lXr55YLBZp2LChQ9v5+flpl0cMCgrSWuYPHDgggYGBOW7ftWtXeeyxx+TcuXPauj///FOaNWsmjz/+eI7bp6WlyYQJE6R06dLac1m6dGmZMGGCYXyuPd27d5f58+c7dN8M2f0/6m87etnGvXv3Svny5cXDw0Puv/9+2bVrl0RGRkpQUJCEhISIp6enLFy40O4+7ty5I8OGDRMfHx/tkpG+vr7y8ssvy+3bt3P1uIjI/ZgVWTErmBVEZMSsyIpZUXizglf1IlOqWLEitm/fjuLFi7u1HomJifjxxx/x7bffYt26dYiOjkbv3r3Ru3dvVKpUyaF91KtXD//+97/Rp08fBAcHY8+ePYiOjkZsbCx+/fVXbNy40e72Z8+eRdeuXXHgwAFERUVp62rWrIklS5bYvJyhrccDACEhITneV/8ryeXLlxEbG4sBAwagVq1aWX4p6Nq1a5btT58+7XC9ypcvb7OsQ4cO8PLywsiRIzF37lwsW7YM7dq1w3//+18AwIsvvogdO3Zg69atOR7n5s2biIuLA6Au45ibXxqIqOBhVmRiVjAriCh7zIpMzIrCmxVs+CFT6du3L7p164Z27dohKCgoX/vy8PCAxWKxWe5Id0p/f3+Eh4fjqaeeQu/evR3q/mdt8eLF6NevH15//XXExsYiJiYGR44cwZw5c7Bs2TK0adMmx32ICFatWqV14axevTpat26d67rkhoeHh0P3s1gsDj2XeVW8eHGsWbMGtWvXRlJSEkJCQrB9+3bUq1cPAHD48GE0atTIoUs3AircAGhhR0SFD7Mie8wKZgURZWJWZI9ZUUizwmV9iYjcICYmRurWrSt+fn7Svn17+fTTT+XPP//M074WLVpk+Pv+++/ljTfekDJlyjg0672IyC+//CJpaWl5Or7ehg0bpHXr1lKiRAnx9/eXxo0bZzspmN7q1aulevXq2V7yMSEhQWrUqCEbNmzI8dgXL16UPn36SKlSpcTT01Prkpjxdy8dOHBAfv75Z1m8eLHhzx7rCfn03VpF1OPL6XGkpqbKm2++KSEhIdrjDgkJkVGjRuWpey4RuRezIhOzQmFWEJE1ZkUmZoVSmLOCDT9kSmfOnJFp06ZJ27ZtxdfXV+rWrSsxMTGya9eufO/7m2++ka5du+Zqm0uXLsnGjRtl48aNcunSJYe3S01NlZiYGDl79mxuqyldunSRiRMn2iyfPHmyQ2Nx27dvLzVq1JBPP/1UFi5cmCW47Pntt9+yjH2ePXu2VKhQQUqUKCHPPfecQ2NZ4+LipHbt2tmOx83p5GqxWAzPeVBQkJw4cUK77cgJevDgwRIRESHTp0+XPXv2yJ49e2T69OlSsmRJGTx4cI71J6KCiVnBrMjArCAiW5gVzIoMhTkr2PBDppeYmCjz58+Xf/7znxIeHi7lypWTIUOGyP79+/O0v7i4OIcmPxMRSU5OlgEDBoinp6d2UvHy8pKBAwc6fAnCwMBAOXnyZK7rWa5cOTl48KDN8kOHDklUVFSO+wkKCspzsLVr107Gjx+v3d67d694eXnJs88+Kx999JGULFlSRo8eneN+OnfuLN26dZPLly9LUFCQHDx4UDZu3CgNGjTI8dcFi8UiHTt2lO7du0v37t3Fy8tL2rZtq93u2LFjjifokJAQ+emnn7KsX758uYSEhORYfyIq+JgV2WNWMCuIKBOzInvMioKfFY4NliMqxIKDg9GzZ0988803uHz5MmbNmgVPT09s2bIl1/u6desWpkyZgjJlyjh0/3//+99Yv349li5dioSEBCQkJGDx4sVYv349XnnlFYf20apVK6xfvz7Xdf3rr7+yvdxiBi8vL1y+fDnH/URFRUHyOBXYnj170KpVK+32vHnz0LBhQ/z3v//F8OHDMWXKFHz33Xc57mfLli2IjY1F8eLF4eHhAQ8PDzRp0gTvvfcehg0bZnfbfv36ISIiAqGhoQgNDUWfPn1QunRp7XZERAT69u1rdx++vr7ZXq6zYsWK8PHxybH+RFTwMSuyx6xgVhBRJmZF9pgVhSArXNakRORGLVq0kGvXrmVZf/36dWnRooVD+wgLC5Pw8HDtLywsTDw9PSU4ODjH8Z8ZihUrJmvXrs2yfs2aNVK8eHGH9vHZZ59JyZIl5ZVXXpFvv/3W4XGo0dHRdi8n+OOPP0rFihVzPP7KlSulbdu2efp1wNfXV86cOaPdbty4sbzzzjva7ZMnT0pQUFCO+wkLC9O6UUZHR8uaNWtEROT48ePi7++f63rlVkxMjPTq1cvQffT27dvSu3dvGTNmjMuPT0SuwaxgVjgTs4LInJgVzApncldWeLmuSYnIfdatW4eUlJQs62/fvp3jZQozTJo0yXDbw8MDJUqUQMOGDREeHu7QPm7evInIyMgs6yMiInDz5k2H9vGvf/0LADBx4sQsZfZmru/YsSPeeusttG/fHn5+foayW7duYfTo0ejcuXOOx3/qqadw8+ZN7TKD1q398fHxNreNjIzEyZMnERUVhZSUFOzcuRMxMTFa+Y0bN+z+epChZs2a2LNnDypWrIiGDRvi/fffh4+PD7744gtER0fnuH1e9OjRw3B71apVKFu2LOrUqQNA/eqQkpJi+OWBiAoXZgWzIr+YFUTmx6xgVuRXQcgKNvyQqezdu1dbPnjwIC5evKjdTktLw4oVKxzuTtmvX7981+eRRx7B6NGjMWfOHO0keevWLcTExOCRRx5xaB/p6el5Ovabb76JBQsWoGrVqhg6dCjuv/9+AOoyg9OmTUNaWhpGjRqV436sgyo3OnbsiJEjR2LChAlYtGgRAgIC0LRpU6187969qFSpkkOPJTk5GQAQGxuLzp07o2nTpihWrBjmz5+f5/rZExoaarj9xBNPGG7zEr1EhRezIhOzIn+YFUTmxazIxKzIn4KQFRaRPA6yIyqAPDw8YLFYACDb8aP+/v6YOnUqBg4c6ND+EhISMGPGDBw6dAgA8MADD2DgwIFZ3ry27N+/H+3atcOdO3cMLbp+fn5YuXIlHnjgAbvbp6en46uvvsKCBQtw6tQpWCwWREdH44knnsAzzzyjPVZbTp8+jRdeeAErV67Ung+LxYJ27dph2rRpqFixokOPI6+uXLmCHj16YNOmTQgKCsLs2bPRvXt3rbxVq1Zo1KgRxo0bl+t9x8fHIzw8PMfngIjIGrPCiFlBRJQVs8KIWVG4seGHTOX06dMQEURHR2Pbtm0oUaKEVubj44OIiAh4eno6tK8//vgD7dq1g7+/Pxo0aAAA2L59O27duoVffvkFdevWdWg/N2/exDfffIPDhw8DAKpXr47evXvD39/f7nYigi5duuCnn35CnTp1UK1aNYgIDh06hH379qFr165YtGiRQ3W4du0ajh8/DhFBlSpVHO5Sau327dtZurqGhITkuN3169cRFBSU5bmPj49HUFCQwxOZHT9+HHFxcXjsscfg7+8PETH1CZqIXINZkT1mBRFRJmZF9pgVhZTLZg8icpOUlBTp37+/NmlXXjVp0kT69+8vqamp2rrU1FTp16+fNG3aNL/VzNHMmTMlODhYm3BMb/Xq1RIcHCyzZ892eT2SkpJkyJAhUqJECfHw8Mjydy9cuXJFWrZsKRaLRTw8PCQuLk5ERAYMGCDDhw+/J3UgInNhVjgXs4KIzIhZ4VzMCvdhjx8ypbCwMOzatStfXQ79/f2xa9cuVKtWzbD+4MGDqF+/vs1J1JYsWeLwMbp27WqzrG3btmjZsiVGjhyZbfm7776L9evXY+XKlQ4fLy+GDBmCtWvXYuzYsXjmmWcwbdo0nDt3Dp9//jnGjx+P3r17u/T4ANC3b19cunQJX375JapXr449e/YgOjoaK1euxPDhw3HgwAGX14GIzIdZ4TzMCiIyK2aF8zAr3MjdLU9ErtC3b1+ZOHFivvYREREhK1euzLJ+xYoVEhERYXM7i8Xi0F9OrdqRkZGya9cum+U7d+6UyMhIhx9PXkVFRWmXjgwODpZjx46JiMicOXOkQ4cOLj++iHoudu/eLSIiQUFBWst8XFycBAYG3pM6EJH5MCuch1lBRGbFrHAeZoX78KpeZEpVqlRBbGwsNm/ejHr16iEwMNBQPmzYsBz38dRTT2HQoEH48MMP8eijjwIANm/ejBEjRqBXr142t8vrbPnW4uPjs71kY4bIyEhcu3bNKcfKqR4ZlzYMCQnRLrPYpEkTvPDCCy4/PgAkJycjICAg27r5+vrekzpkuH37dpbLWBJR4cSscB5mhRGzgsg8mBXOw6wwupdZwYYfMqUZM2YgLCwMO3bswI4dOwxlFovFoRP0hx9+CIvFgr59++Lu3bsAAG9vb7zwwgsYP368S+qtl5aWBi8v229RT09PrV6uFB0djZMnT6JcuXKoVq0avvvuOzRo0ABLly5FWFiYy48PAE2bNsWcOXMwduxYAOr/MD09He+//z5atGjh8uOnp6dj3LhxmD59Ov766y8cPXoU0dHReOutt1ChQgUMGjTI5XUgIudjVjgPs4JZQWRWzArnYVa4Lys4xw9RNtLS0rB582bUqlULvr6+iIuLAwBUqlQp2xZivSlTpjh8HHtB4eHhgQ4dOthseb5z5w5WrFiBtLQ0h4+XFx9//DE8PT0xbNgwrFq1Cl26dIGIIDU1FRMnTsRLL73k0uMD6vKVrVq1Qt26dbFmzRp07doVBw4cQHx8PDZv3oxKlSq59PixsbGYPXs2YmNj8dxzz2H//v2Ijo7G/PnzMWnSJGzZssWlxyeigolZkYlZwawgouwxKzIxK9yXFWz4IbLBz88Phw4dyvVEbo7e32Kx4MSJEzbLBwwY4NB+Zs2a5dD9nOX06dPYsWMHKleujNq1a9+z416/fh2ffPIJ9uzZg6SkJNStWxdDhgxBqVKlXH7sypUr4/PPP0erVq0QHBysTQJ3+PBhPPLII/ekaywRFUzMiuwxK5gVRJSJWZE9ZsW9ywoO9SLTGD58OMaOHYvAwEAMHz7c7n0nTpyY4/5q1qyJEydO5PoEffLkyVzd35Z7feK1tmXLFly9ehWdO3fW1s2ZMwejR49GcnIyHn/8cUydOvWejYUNDQ3FqFGj7smxrJ07dw6VK1fOsj49PR2pqaluqBER5RWzwrmYFZmYFUTmwaxwLmZFJndlBRt+yDR27dqlvVl27dpl834Wi8Wh/b3zzjt49dVXMXbs2GwncgsJCcl7ZQuB2NhYNG/eXDtB79u3D4MGDUL//v1Ro0YNvP/++yhdujTGjBlzT+qTkJCAbdu24dKlS1kmuuvbt69Lj12jRg1s3LgR5cuXN6z/4Ycf8NBDD7n02ETkXMwK52JWZGJWEJkHs8K5mBWZ3JUVHOpFZCU2NhavvPIKgoODtXX6k7qIwGKxODQGduDAgXbLZ86cmfeKulipUqWwdOlS1K9fHwAwatQorF+/Hps2bQIAfP/99xg9ejQOHjzo8rosXboUvXv3RlJSEkJCQgz/HxaLRbsigKssXrwY/fr1w+uvv47Y2FjExMTgyJEjmDNnDpYtW4Y2bdq49PhEVPAwKxRmRSZmBRFZY1YozIpM7soKNvwQWfH09MSFCxdw6NAhu/dr1qxZjvvq3r274XZqair279+PhIQEtGzZEgsWLMhXXV3Jz88Px44dQ1RUFAB1mcUOHTpo3SJPnTqFWrVq4caNGy6vS9WqVdGxY0e8++67OU6C5yobN25EbGysYSzw22+/jbZt27qlPkTkXswKhVlhxKwgIj1mhcKsMHJHVnCoF5lScnIyxo8fj9WrV2fbhc/e5GcZbaGOnIBzsnDhwizr0tPT8cILL7h8xvj8ioyMxMmTJxEVFYWUlBTs3LkTMTExWvmNGzfg7e19T+py7tw5DBs2zG0nZ0Bd+vHXX3912/GJyPmYFfnHrDBiVhCZD7Mi/5gVRu7ICjb8kCk9++yzWL9+PZ555hmUKlXK4fG3GXJ7/9zw8PDA8OHD0bx5c7z22msuO05+dezYESNHjsSECROwaNEiBAQEoGnTplr53r1771nItGvXDn/88Qeio6PvyfGIqGhgVuQfs4KIzI5ZkX/MCvdjww+Z0s8//4zly5ejcePGedq+atWqOZ6k8zP+My4uDnfv3s3z9vfC2LFj0aNHDzRr1gxBQUGYPXs2fHx8tPKZM2fes67rnTp1wogRI3Dw4EHUqlUryy8CXbt2dfoxw8PDHQ5qV48FJiLXYFbkH7OCWUFkdsyK/GNWuD8r2PBDphQeHo777rsvz9vHxMQgNDQ03/WwvvyjiODChQtYvnw5+vXrl+/9u1Lx4sWxYcMGXL9+HUFBQfD09DSUf//99wgKCrondXnuuecAqAnyrDk6IV5uTZo0yen7JKKChVmRf8yKSU7fJxEVLMyK/GNWTHL6PnOLkzuTKX399ddYvHgxZs+enevxmx4eHrh48SIiIiLyXY8WLVrAYrFo43s9PDxQokQJtGzZEgMHDoSXF9teC7tbt27B39/f3dUgojxgVtC9wqwgKryYFXSvuDIr+Oog03jooYcMXeiOHz+OyMhIVKhQIUsXvp07d9rcjzPG4aanp+ODDz7AnTt3kJqaipYtW2LMmDH80JdLW7ZswdWrV9G5c2dt3Zw5czB69GgkJyfj8ccfx9SpU+Hr6+vSegwbNgxTpkzJsj45ORmdO3fG2rVrXXp8InIeZoX5MCuIyNmYFeZT1LOCDT9kGo8//rhT9uOMTnDjxo3DmDFj0Lp1a/j7+2PKlCm4fPkyZs6c6YQaFh2xsbFo3ry5doLet28fBg0ahP79+6N69er44IMPULp0aYwZM8al9Vi+fDnCw8MNVx9ITk5G+/btXXpcInI+ZoX5MCuIyNmYFeZT5LNCiMjpKleuLNOnT9du//rrr+Lj4yNpaWlurFXhU7JkSdm+fbt2+4033pDGjRtrt7/77jupXr26y+tx/PhxKVWqlHz88cciIpKYmCiPPPKING3aVJKSklx+fCIyJ2aFczAriMjMmBXOUdSzgj1+yJTOnj0Li8WCsmXLAgC2bduGb7/9FjVq1MDzzz/v8uOfOXMGHTt21G63bt0aFosF58+f1+pEObt27RoiIyO12+vXr0eHDh202w8//DDOnj3r8npUqlQJK1asQIsWLeDh4YH//e9/8PX1xfLlyxEYGOjy4xORazArzIFZQUSuxKwwh6KeFR4u2zORG/3zn//UxkdevHgRrVu3xrZt2zBq1KhsZ3B3trt378LPz8+wztvbG6mpqS4/tplERkbi5MmTAICUlBTs3LkTjRo10spv3LiRZZy1q9SuXRvLli3DG2+8gYCAAPz888/8IE9UyDErzIFZQUSuxKwwh6KeFezxQ6a0f/9+NGjQAADw3XffoVatWti8eTN++eUXDB48GG+//bZLjy8i6N+/v2FysNu3b2Pw4MGGN/WCBQtcWo/CrmPHjhg5ciQmTJiARYsWISAgAE2bNtXK9+7di0qVKrnk2NaT+mXw9fXF+fPn0bhxY22dvUn9iKjgYlaYA7OCiFyJWWEORT0r2PBDppSamqqdHFetWoWuXbsCAKpVq4YLFy64/Pj9+vXLsq5Pnz4uP67ZjB07Fj169ECzZs0QFBSE2bNnw8fHRyufOXMm2rZt65JjO2tSPyIquJgV5sCsICJXYlaYQ1HPCouIE6YaJypgGjZsiBYtWqBTp05o27Yttm7dijp16mDr1q148skn8eeff7q7ipQL169fR1BQEDw9PQ3r4+PjERQUZDhpExE5illhLswKInIFZoW5FNWsYMMPmdK6devQvXt3JCYmol+/ftrlDt944w0cPnyYXSEpT1JSUnDp0iWkp6cb1pcrV85NNSKi/GBWkCswK4jMhVlBrnCvs4INP2RaaWlpSExMRHh4uLbu1KlTCAgIQEREhBtrRoXN0aNHMWjQIPz222+G9SICi8WCtLQ0N9WMiPKLWUHOwqwgMi9mBTmLu7KCc/yQaXl6eiI8PBzjx4/H4MGDERYWhgoVKri7WlQIDRgwAF5eXli2bBlKlSqV7eRsRFQ4MSvIWZgVRObFrCBncVdWsMcPmV5ISAh2796N6Ohod1eFCqnAwEDs2LED1apVc3dViMhFmBWUX8wKIvNjVlB+uSsrPO7p0YjcgG2blF81atTAlStX3F0NInIhZgXlF7OCyPyYFZRf7soKNvyQqcTGxuLmzZvurgaZzIQJE/Daa69h3bp1uHr1KhITEw1/RFS4MCvIFZgVRObCrCBXcFdWcKgXmYqnpycuXLhgmGTt7NmzKF26dJZL9hE5ysNDtZFbj8HlhJ1EhROzglyBWUFkLswKcgV3ZQUndyZTya4dMyoqyg01ITNZu3atu6tARE7ErCBXYFYQmQuzglzBXVnBhh8yHV5Fg5ytWbNmNsv2799/D2tCRM7CrCBnY1YQmQ+zgpzNXVnBoV5kKh4eHggNDc3xJB0fH3+PakRmdOPGDfzvf//Dl19+iR07drD7PlEhw6yge4FZQVS4MSvoXrhXWcEeP2Q6MTExCA0NdXc1yIQ2bNiAGTNm4Mcff0Tp0qXRo0cPTJs2zd3VIqI8YFaQqzAriMyDWUGucq+zgg0/ZDpPP/20YRI2ovy4ePEivvrqK8yYMQOJiYno2bMn7ty5g0WLFqFGjRrurh4R5RGzgpyJWUFkTswKciZ3ZgUv506mwnG45ExdunTB/fffj71792LSpEk4f/48pk6d6u5qEVE+MSvImZgVRObErCBncndWsMcPmQqnrCJn+vnnnzFs2DC88MILqFKlirurQ0ROwqwgZ2JWEJkTs4Kcyd1ZwR4/ZCrp6ensjklOs2nTJty4cQP16tVDw4YN8cknn+DKlSvurhYR5ROzgpyJWUFkTswKciZ3ZwWv6kVElIPk5GTMnz8fM2fOxLZt25CWloaJEydi4MCBCA4Odnf1iIioAGBWEBFRTtyVFWz4ISLKhSNHjmDGjBmYO3cuEhIS0KZNGyxZssTd1SIiogKEWUFERDm5l1nBhh8iojxIS0vD0qVLMXPmTH6YJyKibDEriIgoJ/ciK9jwQ0RERERERERkUpzcmYiIiIiIiIjIpNjwQ0RERERERERkUmz4ISIiIiIiIiIyKTb8EBERERERERGZFBt+iIiIiIiIiIhMig0/REREREREREQmxYYfIiIiIiIiIiKTYsMPEREREREREZFJseGHiIiIiIiIiMik2PBDRERERERERGRSbPghIiIiIiIiIjIpr7xumJ6ejvPnzyM4OBgWi8WZdaJcEBHcuHEDpUuXhoeHudvx+JojMyhK71mA79uCoii97viaIzMoSu9ZgO/bgqIove74miMzyM17Ns8NP+fPn0dUVFReNycnO3v2LMqWLevuargUX3NkJkXhPQvwfVvQFIXXHV9zZCZF4T0L8H1b0BSF1x1fc2Qmjrxn89zwExwcrB0kJCQkr7uhfEpMTERUVJT2/2FmfM2RGRSl9yxQ8N63aWlp2vLp06cNZdHR0bneBwB4enpqywcOHDCU1ahRQ1t25y+KRel1V9Bec66yezfQrFnetl2/HnjwQWfWhpytKL1ngcL1vv3qq6+05evXrxvK7t69qy0HBgYaysqUKaMtd+nSxTWVy6ei9LorTK85Ilty857Nc8NPxgfYkJAQvlkKgKLQRZGvuXvszBngypXcbVO8OFCunGvqYzJF4T0LFLz3rb7RxjokHa2fvYafoKAgm/ssCP/nBaEOrlbQXnOuYvVSy/W2Jn5qTKUovGeBwvW+9ff315bv3LljKNM3/OjvBwABAQHackF/jEXhdVeYXnNEOXHkPZvnhh8iMrEzZ4D77wdu387ddn5+wJEjbPyhAis1NVVbPnv2rKGsUqVKNrcTEW1Z39Bj7fz584bbtWrVym0ViYgoF/TnZ3tffvT3A4yNNN7e3oYyfQO/l5fx65Kvr69Dx7Muu3Xrlrbcvn17Q9nPP/9scz/6elrXhYjIUeaetYuI8ubKldw3+gBqm9z2EiIiIiIiIiKXYcMPEREREREREZFJseGHiIiIiIiIiMikOFCUiIiKDD8/P235yy+/NJSFhYVpyw9aXfLI3jwOixcv1pYnT55sKGvXrl0eaklERI6yN8dPenq6tuzhYfy923peH72hQ4dqy/o5fQCgVKlS2rKPj4+h7LZumHxKSoqhTH9Bgd27d9s8tjX9vD72Li5ARGQPe/wQEREREREREZkUG36IiIiIiIiIiEyKQ72IiKjI0F/OfePGjYay7du3a8u1a9c2lA0YMEBbjo2NNZTpu/bXrFnTKfUkIiLH6Idw6c/xgP3hXD/99JO2/OGHHxrK4uLitOX77rvPUKYfTlamTBlD2fnz57Vl62FZ+u2sLy2vHz42YsQIQ9nLL7+sLXNoFxHlFXv8EBERERERERGZFBt+iIiIiIiIiIhMikO9iIiIiIiIiCiLM2eAK1dyv13x4kC5cs6vD+UNG36IiKjI0M/3ULJkSUPZ3bt3teXDhw8byoYMGaIt6y8JDwDh4eHacokSJZxSTyIicoz+ku325vTp1auX4fZ3332nLQcFBRnKAgICtGXrS8QnJSVpyxcuXLB5vFu3bhlu+/v7a8vW8//cuXNHWx41apSh7IMPPtCWp06daih78skntWV9hgHGy8AT5dWZM8D99wO66Qwd5ucHHDnCxp+CgkO9iIiIiIiIiMjgypW8NfoAaru89BQi12DDDxERERERERGRSbEPIBERFUn6bvcAcO7cOW05ODjYUBYWFqYt+/r6Gsr0l3MPDAx0Yg2JiCg/1q5dqy0vWrTIUFa+fHlt2foy8NbDpvRSUlK05VOnThnKatSooS3rh28BQEJCgrZsPWRYf9s6R/R1GzhwoKHswQcf1JYrV65sKNNfMt56uBoRFT3s8UNEREREREREZFJs+CEiIiIiIiIiMik2/BARERERERERmRTn+CEioiJJPxcDAMTFxWnL9i4JbF2mn+OnTJkyNrfTz7cAcM4FIiJn8PCw/Tv2559/ri17enoayvTz+FhfXl1/vtZfLh4wZoB1Hpw/f15btp4Pzt6cO/oy6/mF9Mewfqz//ve/teWlS5caypgxRKTHHj9ERERERERERCbFhh8iIiIiIiIiIpPiUC8iIjIte8OrrC+Z6+WVGYn2touMjDSUXb161eZ2RER071ifgzdt2qQtBwQEGMr0l0m3N/TK+lLv+iFc1sPH9MPCkpOTDWX+/v4262kvO/RDv0JCQgxlGzZs0Jb37dtnKKtVq5bNfRJR0cMeP0REREREREREJsWGHyIiIiIiIiIik2LDDxERERERERGRSXGOHyIiMi17l7M9fvy44ba9SwLfuXNHW75x44ahrFixYtry6dOn81QXIiLKv/nz5xtux8fHa8vW8+Po5+OxPj+HhoZqyzdv3jSU6ef8sb4MvH7uOOvLwOtzxM/Pz1CmP769+X7szQ300UcfGcq++uorm/shoqKHPX6IiIiIiIiIiEyKDT9ERERERERERCZlyqFen376qeH2/v37bZbZo+8+yS76RETmsnbtWsPtcuXKacve3t6GMusu+3r6fDh8+LCTakdERLn122+/GW7rL7dufVl2PR8fH8PtW7du2dxOnw/6S60DQFhYmM1j6L9XWA8R0w81th7Opc8Y6+PpH9/GjRttHpuIiD1+iIiIiIiIiIhMig0/REREREREREQmxYYfIiIiIiIiIiKTctocP/qxsADg7+/v0H2tx9Taox/Ham3ZsmXa8vnz5w1lERER2nLfvn0NZePGjdOWo6KiDGX25vWxHpvraD2JiMh9jh07pi2XKFHCUObr62tzO/2lfa2zQX/7woUL+a0iERHl0c6dOw237c2do/8OYn1ev337trZsfel1/Tw71tvpM8C6zN53npSUFJv30x/P+jHocysgIMDm/omI2OOHiIiIiIiIiMik2PBDRERERERERGRSThvqZT2EaujQodpys2bNDGX2hoHllf4y7Q0aNDCU6btMli1b1lA2f/58bVk/JAwAunfvri0HBwcbyvTDuayHfVl3w3QULxlPRORa+mEA+q71gPEcbO/yvfohAIBxKMGff/7plHoSEVHuxcXFGW7rP69bfz5PT0/XlvXncQDw8sr8imR9CXX9fvT3s96PdY5Y78fWdvbuZ/2dQ3/8pKQkm9sREbHHDxERERERERGRSbHhh4iIiIiIiIjIpNjwQ0RERERERERkUvme4yc5ORmenp5Z5jVYsmSJtnzz5k1DWc2aNbXl++67z1CmvxShfuwtAJw5c0ZbnjVrlqGsZMmS2nLx4sUNZUuXLtWWu3XrZihLSEjQln/66SdD2eHDh7Xl6OhoQ1mbNm205fLlyyMvrMfp2htrzEvEExHl3++//64tW59n9edk67kg7F0SWD83UKlSpQxlx48f15YrV66chxoTEZGj/vrrL8Nt/XeCvM65Yz0Hp/629T70n+Wtt9Pv0/o7jn4eOet6OjoH6KlTpwy3ExMTteWQkBCH9kFE5sUeP0REREREREREJsWGHyIiIiIiIiIik8r3UK9Dhw4hMDAwy/rk5GRt+ZtvvjGU1a5dW1vWX2rd+ra+izwA7Nu3T1u2vgxv06ZNtWX95XoBoF27dtqyfiiZ9fHat29vKLt06ZK2fPToUUPZli1btOXq1asbyh544AFtuX79+oayEiVKaMvWw7c4nIuIyLUOHDigLVsP9dLngfVlce117bd3GfirV69qyxzqRUTkWtZDcfWfra2/O+jP3dbTL+jL7A21sh6ypR9aZj3Vhf629Wd+6/3o6etm737Wjhw5oi0//PDDDm9HRObEHj9ERERERERERCbFhh8iIiIiIiIiIpNiww8RERERERERkUnle46fhIQEpKamIj4+3rhjr8xdX79+3VC2cOFCbTk8PNxQpp8fITg42FD2yCOPaMtVq1Y1lOnnatBfLh4Arly5oi1bj7fVX07e+jHo5wMqV66coUx/W3+5RADYuHGjtrx9+3ab+wwLCzOU6S8LHxERYSirVq2atuzr6wsiIso9/eVureeY08/dYz2Pjz7TrOeJsLUPADh27Ji23LBhw1zVlYiIcnbu3DmbZfr5eazn/3EG633q5+CxzgP9dxzrOebs0W9nnVv2HtPJkye1Zc7xQ0Ts8UNEREREREREZFJs+CEiIiIiIiIiMql8D/UKDAxEYGBglsudDxgwQFuuUKGCoUw/pOr27duGMv3wJz8/P0OZ/r579+61WaegoCDDbf3wKusu+hcvXtSWrbtPhoSE2NxOP7xLf+lGwDh8zJr+MegvFw8A58+fz7bOAPDOO+9oy3369NGWrYeuERGRbWfOnNGW77//fkOZ9aXY9fTDBfTDvgBj137r7vv79u3LUz2JiMgx+suW22N9KfTcDLeyxfqy7FevXrVZpv9eY10XezmiHzJm/V3F3uXdL1y4YLOMiIoe9vghIiIiIiIiIjIpNvwQEREREREREZkUG36IiIiIiIiIiEwq33P8/PTTT/D19UWpUqUM6/Xz1Vhf7jw6Olpbtr5Mun4cq34fAHDnzh1tOS0tzWadEhISDLf1l5P39vY2lOkvm25vjh9r+jl4IiMjDWX6ulnPDaQf32s9h5H+ebJ+7Pr5JSZOnKgtW18qkoiIMllnhX6eNet5FOxdpl0/j4L+fAwYs8l6Tgf9PHJEROR8J06ccOh+1vOz6S+Fbn1e1+eD9SXTre+r5+vrqy1b548+R+zt0zqb9Letj21vjp/Lly/bLCOiooc9foiIiIiIiIiITIoNP0REREREREREJpXvoV4nTpyAt7c3KlWqZFhfs2ZNbXn//v2Gsj///FNbth7SpB9CZa/7onWZvou+dXd9fbdIfRdMwNgN0rrM399fW7YeIqZ35coVm3W7ceOGoUw/DM26TH8ZeuvL3B87dizbfdh7joiIirrTp0/bLNPnDQAkJydry9bnfHtd9PW3rYfw6i8fT0REznfp0iWH7mc9TEo/FMt6mK6jrPPA0eFc9upi/X1EP9QrNTXVUGbv+0l8fLzNMiIqetjjh4iIiIiIiIjIpNjwQ0RERERERERkUmz4ISIiIiIiIiIyqXzP8RMQEABvb29s3brVsF4/z471nAf6sps3bxrK9JdQL168uKEsKSlJW7Z3OXfrcbr6yzdaX8pRP27W+nLuetZjaPXz8ViPLdY/JutL2evn7rEep6uvm/Vl2vXbxcTEaMu3bt3C4MGDbdabiKgoO3z4sM0y60vm6s/J1nmgv691/ujncbDOmHPnzjleWSIiyrW4uDibZfpzt/X3g1u3bmnL9ubKscd6rs3SpUtry1evXjWU6T/LW8/xo88O67lKw8PDbe5TX2/r/OHl3IlIjz1+iIiIiIiIiIhMig0/REREREREREQmle+hXu+//z5CQkJQrlw5w/r77rtPW7a+3Lm+C6N1d3r9MCnryxAGBwdry9ZDofRdOa27Ouq7Yeq7dQLGrpbWddHX03pIgP4Y9sr0zwMAhIWFacvWQ+D0973//vsNZW3atEF2EhMTOdSLiMiG3Ay1ss4VPXuX6NUPEbPuvn/jxg2Hj09ERLmn/55h/R1Af362Hpalv6/1eV3Pukx/2/o7wIULF2weT8/ed4fr168bylq0aKEtL1++3FCmzy3r4WrWw8KIqGhjjx8iIiIiIiIiIpNiww8RERERERERkUmx4YeIiIiIiIiIyKTyPcdPeHg4QkJC8O677zqjPkRERE5jPceOfh4F/dwPgHE+Buv5fvRl1vP46FnP22Bv3iAiIso//Xneep4b/dyh5cuXN5Tp5w79/fffDWVlypTRlu/cuWMos5cB9sr0rOf/0WdHUlKSze30l3YHjPP4WM9vlJaW5lBdiKhoYI8fIiIiIiIiIiKTYsMPEREREREREZFJ5XuoFxERUUFlfTl3/TAA6672+m7xuRnOpb9tvU/9EAHroWXWQxKIiCj39EO9/P39DWXx8fHa8oMPPmgo0w+N2rp1q6FMf8l2e3lgfal3e8N79fux3qf+tvU+9cO7qlataihbtWqVtly8eHFDmb3LyRNR0cMeP0REREREREREJsWGHyIiIiIiIiIik2LDDxERERERERGRSXGOHyIiMq3ExETDbV9fX23Zeh4FPU9PT8Nte/M96Of4sZ7/R08/1wQAREZG2rwvERE5Rj8/m72501q0aGG4feDAAZv3tXcu17POA/0l4vWXkgfsz/9jT7FixbRl63l89HP8WNfZXsYRUdHDHj9ERERERERERCbFhh8iIiIiIiIiIpPiUC8iIjKtpKQkw21HL6Fu3UVef9t6GJi9feov4Z6QkGAo41AvIqL80w/h1Q/7statWzfD7d27d9u8r/7cbX1ZdP2QKusyfVakpKTY3M46Y+7cuWOzLj4+PtryY489Zih77733tGXrYWchISE290lERQ97/BARERERERERmRQbfoiIiIiIiIiITIoNP0REREREREREJsU5foiIyLRu375tuB0YGKgtW88Fob9tPW+D/jK8+vkkAOOcP9ZzOlSsWNFmXYiIKP/0c+BYCwoK0patL4WenJysLVvPj6PPAOs8sOfGjRvasvXl1fUZY30869t6+rl6rPNHn03W9bQ33xERFT3s8UNEREREREREZFJs+CEiIiIiIiIiMikO9SIiItPavHmz4XZwcLDN+/r7+2e7DBiHElhfvl3fRd/6Er364V1HjhwxlNWpU8dmXYiIyDH6IbxJSUmGMntDbPXncushVPphWtZDtvTDe+0NEbM3nMt6n15emV/J/Pz8DGWJiYnZLluzfgzFihWzeV8iKnrY44eIiIiIiIiIyKTY8ENEREREREREZFIc6kVERERERERUwIwcOTJP240fP97JNaHCjg0/RERkWoMHDzbcfu+997Rl60uv6y/De+HCBUPZfffdpy2npqYayvTz/1jPIXTz5k1tOTw83NFqExGRg3766Sdt+cqVK4ayW7du2dzu+PHjDu3f+rLo+tvW87rp5+qxnuNHPzeQ/jLs2e1Hb+/evdryW2+95fB2RER6bPghIiIiIiIiogKJPZ/yj3P8EBERERERERGZFHv8EFGBdeYMYNVrO0fFiwPlyrmmPlT4xMbGGm7XqlVLWz548KChTD8koGrVqoayBx98UFvWD98CgICAAG3Z+pLtvXr1yl2FiYgoz4oXL+7wffXDdK0voa6/1Lt+GTAO97UeaqXfj/UQMXvDsvT3tR4yXK1aNZvbERE5ig0/RFQgnTkD3H8/cPt27rbz8wOOHGHjDxEREREREcChXkRUQF25kvtGH0Btk9teQkRERERERGaV5x4/Gd0VExMTnVYZyr2M578ozOrP19w9lJSUv22d8H/kjCqMHj06T9vHxMTk/eA5KErvWaDgvW/1w7RuW7Us3rlzR1u2vhJMku4FaV2Wnp6e7f6BgvO4i9LrrqC95lylAJymyYWK0nsWcM/7Vn9lLevnWX9et6Yvs7ed9T7097Uu018BzLpMn02ufn6K0uuusGSF/v8/N5z1uPKTNRnbO6Mq7n4eCqrcvGctksd39p9//omoqKi8bEoucPbsWZQtW9bd1XApvubITIrCexbg+7agKQqvO77myEyKwnsW4Pu2oCkKrzu+5shMHHnP5rnhJz09HefPn0dwcLChlZruLRHBjRs3ULp0aXh4mHvkXn5ec4mJiYiKisLZs2cREhKSp+Pndx9mqIMZHoO761CU3rMAs6KgKEqvO2aF++tghsfg7joUpfcswKwoKIrS645Z4f46mOExuLsOuXnP5nmol4eHh+lbgguL0NBQd1fhnnDGay4kJCTPb2pn7cMMdTDDY3BnHYrKexZgVhQkReV1x6woOHUww2NwZx2KynsWYFYUJEXldcesKDh1MMNjcGcdHH3Pmrspl4iIiIiIiIioCGPDDxERERERERGRSbHhh4oEX19fjB49Gr6+vm7bhxnqYIbHUFDqQEQFj1nOL+6ugxkeQ0GpAxEVPGY5v7i7DmZ4DAWlDo7I8+TORERERERERERUsLHHDxERERERERGRSbHhh4iIiIiIiIjIpNjwQ0RERERERERkUmz4ISIiIiIiIiIyKTb8EGUjNTUVAwcOxMmTJ91dFSoAzpw5g+zmwRcRnDlzxg01IqKCgFlBeswKIsoOs4L03JUVzm346d8fePxxx+9/6hRgsQC7dzu1Gi519SoQEaHqXtDk9vl31NNPAx995Pz9utiRI0cwdOhQtGrVCq1atcLQoUNx5MgRh7b19vbGjz/+6OIaFh0bNmzA3bt3s6y/e/cuNmzYYHfbu3fvIjY2Fn/++aerqpejihUr4vLly1nWx8fHo2LFim6oEZnBY48B337r7lrkLCUFqFAB+OMPd9fENZgVBQezgoq6gvg1o1EjgKc5ZkVBwqzIm4Lf4+evv1SDRunSQEAA0L49cOxYZnlG41F2f99/r+4THw906QIEBQEPPQTs2mU8xpAhjjdsjBsHdOumPgVnWLhQnRVDQ4HgYOCBB4CXX877Yy5o3nxTPe7r191dE4f9+OOPqFmzJnbs2IE6deqgTp062LlzJ2rWrOnwiffxxx/HokWL8lwHV7XuJyYmYtGiRTh06JBD9/f09MSlS5eyrL969So8PT2z3Wbv3r0O/zmiRYsWiI+Pz7L++vXraNGihd1tvby88MEHH2R7gr9XRAQWiyXL+qSkJPj5+bmhRq7Xv3/mqdTbG4iMBNq0AWbOBNLT3V07o9u3VX1r1QK8vGy3f69bB9StC/j6ApUrA199lfU+06ap07ufH9CwIbBtm7F8+HDgvvuAqCjgm2+MZd9/r6LGEUuWqHh7+unMdRUqZD7n/v7qds+ewJo1ju3TVXx8gFdfBf7zH/fWwxWYFZmYFflXFLMity5eBF58EYiOVufiqCh13ly92rnHqVABmDTJsfo88wxQsiQQGKgyIru3/vLlKhP8/YHwcGPOuPprBqDq1Ly5+qoRFATUrg3ExqpjO8tXXwFhYVnXv/kmMHJkwcv+e4lZkYlZkX/uygovl+3ZGUTUmdXbG1i8GAgJASZOBFq3Bg4eVGfoqCjgwgXjdl98AXzwAdChg7o9bhxw4wawcyfw2WfAc89l/nS5dSvw++/AlCk51+fmTWDGDGDlysx1q1cDTz2ljtG1q/rEfvAg8OuvTnkK3ColRX3ir1kTqFQJ+PprlV6FwGuvvYbXX38dsbGxhvWjR4/Ga6+9hieeeCLHfVSpUgWxsbHYvHkz6tWrh8DAQEP5sGHD7G6f0br/1ltv5f4B6PTs2ROPPfYYhg4dilu3bqF+/fo4deoURATz5s3L8bFk15UQAO7cuQMfH59syx588EFYLBab22aUWSwWpKWl5fgYbJ3grl69muV5zU7Lli2xfv16VLD+JORiw4cPB6Ae71tvvYWAgACtLC0tDb///jsefPDBe1qne6l9e2DWLCAtTTVSrFgBvPQS8MMPquHCy0aCpKaq0/a9kpamPowPG2b7V8mTJ4FOnYDBg1WDzerVwLPPAqVKAe3aqfvMn68adqZPVx/wJ01SZUeOqF9gly5VPXR++UX9/jBwoCovXly1i48aBaxa5Vidp0wBBgwAPKx+fomNVRGVkqJ+1/j6axV5Y8eq/WdHRD0Htv4/nKF3b+CVV4ADB9RvG2bBrMjErMi7op4Vjjp1CmjcWDUufPCBaqxPTVUfq4cMAQ4fvvd16tsXSEhQmVa8uDrH9+ypviY89JC6z48/qvPyu+8CLVsCd+8C+/dn7sOVXzMAde6fMAH4979VHUqXVhk0fTowd67KZVfq0EHl5c8/qxwtipgVmZgVeefurPDSmtrff181IQOqyffll9Wn4JdfBs6eBZo0Ud8ASpXKqB0wYoT6+dfTExg0SH361FuxAnjnHXV29PQEHnkEmDxZNSI44tgxdcbcvz/zk+Znn6lm+f/9T52FPD3Vbb2FC9VZO+PxHDqkflatWhV4/nnVMASotBk8GPjyS7WfnPz0k/p5olGjzHVLl6oUGzEic13VqsafAsaMARYtUp+a33oLuHZNnUX/+1/VQwhQzegTJqi6Xbyo9vHWW8CTT6rytDRV9zVrVHm5csC//mX/bL99O9CxY+ZPtQkJannxYuDOHaB+feDjj4E6dYz1HDpUpdjp05nN+126APPmFZqGnwsXLqBv375Z1vfp0wcffPCBQ/uYMWMGwsLCsGPHDuzYscNQZrFYcjxBA5mt+//+978dq3g2NmzYgFF/f+NbuHAhRAQJCQmYPXs23nnnHZsn6Cl/f8qwWCz48ssvEZTxfoA6uWzYsAHVqlXLdltn/ZrQo0cPrQ79+/eHr6+voQ579+7Fo48+muN+OnTogJEjR2Lfvn3ZhmXXrl1z3MdDDz2UbUhYLBb4+fmhcuXK6N+/v+GXgl1//2wnIti3b58h0Hx8fFCnTh28+uqrOR67sPL1zTy9limjfglt1Aho1UrFxLPPqjKLBfj0U/WhcPVqdTocM0adamJiVFt46dJAv37qA6yXl4qLmBgVIX/9BRQrpk53GR+OP/1UnZ7OnlW/cDZtqhqcshMYqKIBADZvVqc6a9OnAxUrZv7qWr06sGmTOkZGw8/EieoD+4ABmdssX67qOHKkipLmzdWps359FY8nT6ovC6+9Brzwgjo15+TyZXUqnzw5a1lwcOZzXq6cGg5WqhTw9tvq+bn/ftVzqUULFUlvvgns26caox57zH6MXLumTu+//AIkJQFlywJvvKEeb0qKavT68Ud1v8hIFY+vv662DQ9XUTdvnmqEMgtmBbPCGrPCdf71L5UX27ap83aGBx5QDekZzpxRvYJWr1aN4+3bA1OnqvMSAMTFqfPV1q1AcrI6n7/3nmokB9R5+vRp1VCS8Zay8X0Tv/2m8qNBA3X7zTdVLuzYoRp+7t5VH7U/+EB91clQo0bmsiu/Zmzbphp7Jk0yfuSvUEH1wtXn3WefAR9+qHKzYkX1WJ55JrN84kT1Ve7ECdVzVf/1b926zOzLePmPHq2y3NNTfZ2YN6/oNvwwK5gV1gpjVnhh9mx1Jn7tNfVJO8PNm+rsMXeuOuv26aMaDTL6tn/0kfrkP3OmOuN+9JFqcGnZMnMfycnqzFy7tvqU+fbbQPfuak4f6585s3PnjvpX3+XJw0OdFTdtyvzWobdjh9r/tGmZ6+rUUZ+yn31WNaPXrq3Wv/9+5qd4R2zcCNSrZ1xXsqT6eWD/ftUzxpa4ONWosmyZ+lTdsycwfrxqYAFUYn39tfqmUaUKsGGDes5LlACaNVMNMGXLqrEExYqppHr+efWNoGfPrMdbswbo0UM9xuefV+v+8Q/1s/jPP6tvUp9/rr7BHT2qEgAAjh9Xn/wXLDCmVIMGqq537qjnv4Br3rw5Nm7ciMqVKxvWb9q0CU2bNnVoH844SeW3dR9Q3Rbv+/v/Z8WKFXjiiScQEBCATp06YYS+wdHKxx9/DECdXKZPn27ofunj44MKFSpg+vTp2W5bvnz5HOvliNDQUK0OwcHB8Pf3N9ShUaNGeO6553Lcz7/+9S8AwMSJE7OUOfrrQPv27fHZZ5+hVq1aaPD3J7zt27dj79696N+/Pw4ePIjWrVtjwYIF6NatGwBg7dq1AIABAwZg8uTJCAkJyfE4ZteypTqlLlhgPAWPGaNOaZMmqYadjRvVL6lTpqhGm7i4zFPR6NHqNPPxx+qD5AMPqIaKPXtU+R9/qN47c+cCjz6qurJv3Ji/em/ZkvmlIEO7dpmjclNSVHxkNHQAKm5at1bbAupxf/GFOoWfOAHcuqWGjG3apH7p1UeoPZs2qZHL1as7dv+XXlKNLYsXq6jOMHKkiunoaNUwk1OMvPWWaoT7+WfVWHX8uHoMgPp/WrIE+O471eB09qz602vQIP//DwUNs4JZYY1Z4Rrx8er34HHjjI0+GTKGGKWnq6FOQUHA+vWq4WXIENW5ft06dZ+kJNUQMW6c+kg6Z45qxDhyRJ2/FixQ5+vnn1eN+fY8+qj6nbtTJ1WH775Tw4ebN1flO3cC586pPHjoIZVVDz6oGoIyPva78mvGN9+o5+Lvl7bN523hQpUVkyap3Fq2TDXklC2rfigA1GOYMkU1Cp04Yfz69+ijatu331bPI5D5+zmgzv/jxzv2OMyIWcGssFYos0JERL7/XqRYMdHMmiUCiBw/nrlu2jSRyMjM26VKibz/fubt1FSRsmVFunUTmy5fVvvdt0/dPnlS3d61K/v7p6SIlCsn8o9/iMTHi9y5IzJ+vNqmbdvst3nhBZHq1Y3rEhJEevVS+3rsMZEDB0SOHhWpUkXkyhWR//s/kYoV1XESEmzXv1s3kYEDjeuSkkQ6dlR1Kl9e5KmnRGbMELl9O/M+o0eLBASIJCZmrhsxQqRhQ7V8+7Yq/+03474HDVL1tmXIEJEnnsi83a+fquOCBSJBQSLz5mWWbdwoEhJirJeISKVKIp9/nllPb2+RS5eyHmvPHvUYT52yXZ8C5LPPPpMSJUrIkCFDZO7cuTJ37lwZMmSIREREyGeffSaLFy/W/nJy584dOXz4sKSmpua6HhUqVLD5V7FiRYf2UaVKFZk/f74kJSVJiRIlZPXq1SIisnv3bimmf9/a0Lx5c4mPj8913a0dOHBAfv75Z8Nz58jzl56eLv3795cbN27kuw758eyzz0psbGyW9WPHjpVnn31WRETefvttqVevnt39nD17Vs6ePeuSOhYkGaeT7Dz1lPE0C4i8/LLxPq1aibz7rnHd3LkqOkREPvpIpGpVdZq39uOP6nSlP2Xmt95VqmStz/Llqu43b4qcO6eWrU/DI0aINGiQeXv0aHXarFlTnWrv3FHLf/whMnWqekyPPiqyf7/tOn78sUh0dNb15cursuxERqp4ExFZu1bVddGizHJHYqRLF5EBA7Lf/4svirRsKZKebrvekyeLVKhgu7wwYlZkYlYozArX+P13dd5asMD+/X75RcTTU+TMmcx1Bw6obbdts73dAw+oc3AGe+dTvWvX1FcKQMTLS2XPypWZ5f/7nyorV07khx/Uub5XL/W16epVdR9Xfs3o0EGkdu2cH8ejj4o895xx3T/+ob6i2JLd17/Q0Ozvu3ixiIeHSFpaznUxI2ZFJmaFUhizAhIUJOLnp85qyclq7axZ6hOk3oIFIhaLWk5IUPdfv954n8cfN37iPnpU5Omn1dkuOFgkMFBtt3y5Ks+p4UdEnWHr1FH38/QUaddOnQXbt89635s31Rnrww9zfuQtWqhPzZMni7Rpo7599OsnMny47W3athX517+yLzt+XOS//1WfssPC1Fk64/kcPVqkRg3j/SdOVM+LiPqGAKjnR//n7W38xvHJJyJ164oUL55Z/vDDmeX9+omULKmep4ULjcf75BN1xrY+hoeHyGuvZdazcuXsH9/Ro6qOBw/afn4KEIvF4tCfh4eHzX0kJyfLwIEDxdPTUzw9PSUuLk5ERIYOHSrvvffevXooMm3aNPHy8pKwsDCpU6eOpP2dulOmTJHmzZu7/PhxcXFSu3Zt7fnSP3f2nr8MaWlp4u3tLUePHnVKfW7dupWn7UJCQuTYsWNZ1h87dkxCQkJEROTQoUMSFBSU5T5paWkSExMjISEh2uMODQ2V2NhY7f/DbOw1/PTsaTylASJff228T/HiKlr0pxt91Jw5IxIVpX4vePZZFTEZn4ESE0Vq1VL76NNH7TvjdJrXejur4cfamDGq0WvPHtU4c+mSyMyZ6lRty7vvZo0EEftfVCIiMuMno+Hnzz8zyx2JkZ9+EvH3V5E6YoTI5s2Z2+/YIXLffep5evFF4xefDF98oephJswK52FWFM2scNTWrY41/NhqYA4LE5k9Wy3fuCHyyisi1aqpj/0ZH2dHjMi8v6MNP0OHqnPkqlUiu3erc3poqMjevar8m29UvTN+IxVRDe3Fi4tMn257v876mtG+vWMNP+HhIl99ZVw3aVLmVw0RkV9/VQ38pUuLza9/thp+fvklMy+LImaF8zAr3JcVHtixI3NYVEpKZlcg65k5LRbbA2Rt6dJF9e3873/VzGa//571ODmpV08N3UpIUJM4r1ihrnUYHZ31vj/8oIaoZTMG02DWLNU3sls31W80YwLpf/wjsx9pdooXV338s1Opkurj+eWXql/owYOq72iG7J7PjPlzkpLUv8uXq8ea8XfwYOakFvPmqaF2gwapCRp2786cmMG6HtWqqSF4qamZ65OS1LAw/f5371b9OfXd+mxNiJUxc3qJEtmXFzDp6ekO/dnryvf6669jz549WLdunWGG9datW2O+/v/WASkpKThy5EieZpD/17/+hS1btmDmzJnYtGkTPP4eJhkdHY133nnHoX38+eef+PTTTzFy5EgMHz7c8JeTl156CRUrVsSlS5cQEBCAAwcOYMOGDahfvz7W2Xu//M3DwwNVqlTB1atXHaprdtLS0jB27FiUKVMGQUFBOHHiBADgrbfewowZMxzah5+fH3777bcs63/77Tft/zc9PT3b2fRHjRqFTz75BOPHj8euXbuwa9cuvPvuu5g6dWq+J9krjA4dUl3F9axPHUlJag4f/elm3z41dZufn5qX/8gR1cXc3191OX/sMXXaCg5Wp9H//S9zfps6dbKfu8dRJUuquYT0/vpLXTPA31+d3j09s7+P9TRyGQ4fVkOrxo5V0fHYY+oU2bOnqv+NG9lvZy9KsnP1qpoXyN5z7kiMdOiQOe/F+fNqpG/GUPK6ddV8RWPHquFfPXtmzg2UIT6+0ESAw5gVRswKZoWrVKmiPvo6YwLnV19VQ5vefVcNj9q9W00UnZuvF4AagvzJJ+ojc6tWKmdGj1ZDszK+GmVMbaqf08fXV30NOXMm+/0682tG1apqWJb+I31enDoFdO6shqH9+CNsfv2zJT5eZY5uZE2RwqwwYlYU0qwQEZGxY1Uz7rVrqjkouybfhQvVfTJkN9QrKirzp9YrV9T9N2zIvM/GjWpdRm8UR3r8WDt6VDXrZ/dzZLNmxqFP2bl0Sf2UkPFTadeuqklcRNWrTh3b237wgf3yDOnpqq9oRp/T0aOzbvfxx+rnCBH187avr8icObb3OXSoaqbXa9XKuN+Mn7r/+kuNw3j88cxxFBl9Z0+etH2M7OqZ4csv1U/zRUi5cuVky5YtIiISFBSktcwfO3ZMgoODHdqHK1r37969K7t27XK4m+WqVaskICBAatasKV5eXvLggw9KWFiYhIaGSosWLXLcvlixYrJnzx4RUa3bhw8fFhGR1atXy4MPPuhQHZYsWSJNmjSRfRnDPHMpJiZGoqOj5euvvxZ/f3/teZw3b540atTIoX2MHTtW/P39ZdiwYVo33WHDhklAQIC88847IiIyceJEad26dZZtS5UqlW3300WLFknp0qXz9JgKOls9Z1avVqftmTMz1+lP6xkefTRrl3V7Dh9W+9mxI2tZUpLqgv/jj3mv92uvqSFZer16qU6kGRo0UKfaDGlpImXKiGT3Vk1PV136lyxRtydOVKdcERWl+ki1tn276kBr/Ra29Qv1W2+p03fGD0sZPX70+3ckRqxNn64642ZnxQp1jIyhDCKq91WfPo7vv6hgVijMiqKZFbnRvr06pyYlZS3LOJ/ZG+q1fbu6XbOmiH6ExY0b6mvLSy9lrqtSJecBAHv3Zt+ZvW3bzGFT16+rc+uXX2aWp6So3o/6XkAZnP01I6OnVMY+rGU8b7aGenXqpJZ/+EH1ANV3JrD++vfNN6onUHbefFOkSRPbdaecMSsUZoX7sgIyZ446C+e24Wf8eNUvfOFCkUOH1NkmODjzE3damho42qeP+rS6erUalpTbhp/vvlOfcuPiVJ/J8uVFevTIer9jx9Qn6Z9/tv+I//lP4yDgCRNE6tVTZ/0OHWwP5RJRCeHlZfy0Pnq06lu6dq3IiRMiO3eK9O+v+tP//ULOseFHRGTUKPV8ffWVGja2Y4fIlCmZ/TYnT1aNSStWiBw5os7AISHZN/yIiFy4oPrAPvGEapRLT1dn7Dp1VKPZyZOqn/8bb2Qmqb2Gn379cvctrgBYt26ddO7cWSpVqiSVKlWSLl26yAZ9Q2QO9CcC/Ql69+7dWhe+nAwbNkzq1asnGzdulMDAQG0fixYtcvjk9tJLL8mXf3/iuHv3rjRu3FgsFosEBgbK2rVrc9z+4YcflrffftvwOG7cuCFdu3aVTz/9NMftw8LC5MSJEyIiEh0dLWvWrBERkePHj4u/v79DjyEsLEx8fHzEw8ND/Pz8JDw83PCXk0qVKsmqVasMj0FEdaEMCwtzqA4iIl9//bU0atRIO26jRo3km2++0cpv3ryZbZdPX19fOXLkSJb1hw8fFj8/P4ePX5j066c+pF+4oD7A7tghMm6c+lDYubPI3buZ982u4WfFCnW6HDNGDUM6eFDNlTBqlCqfNUt9kN63T53e33xTnTavXBFZulSd8nbtUtOKffqpau+3N2/OgQPq/l26iDRvrpb10XLihBrBPGKEiqxp09SXixUrMu8zb576gP/VV6q+zz+vhhdcvJj1eF98Yfyd4fff1Sl5yxaRt9/OfihXhrt3RUqUUI9Tr3x59WXmwgX1pWf9ehWtFouK3AzZNfyI5Bwjb72lYvTYMfVcdu6cOQzso49Evv1WPTdHjqhRyyVLGr8klC+fu4alwoJZoTArMjErXCMuTp1XatRQDRFHj6pz7eTJ6iOriPq4+uCDIk2bqnPY77+rj+nNmmXup3t3dZ9du9TwrC5d1FcQfcNPmzaq0eXPP9UUo9lJSVEzHDRtqo5z/LhqLLJYMmelEFH7LVNGfXw+fFidHyMisjbeizj/a4aI+uHC01Pl12+/qVxctUrkySeNjUre3iovjx5V53RPT5UXIup5ymhAiouTbL/+bd6sbq9apZ4z/RDrZs2MjW1FEbNCYVZkKmxZAWnXTr37c9vwk5qqzoQhIeqT8fDhIn37Gn9q/fVX1fPE11cNUF23LvcNP5Mnq54m3t5q1rQ331SzaVp7/XXV48jeuLgVK9SnXP19kpNVk3hwsOpB89dftrcXUdvrB/WuWaM+/UdFifj4qEke2rdXvZsyONLwk56uzsb3368ea4kS6qfojHmUbt9WDUqhoer5fuEFkZEjbTf8iIicP69mGu3ZU33TSExUkzeULq2OERUl0rt35s8qthp+bt1Sx/27lbowmDt3rnh5eUnPnj1l8uTJMnnyZOnZs6d4e3sb3pD2NG3aVKZMmSIi6qSQcZIaOnSotNN3E7DDGa37ZcqUke1/N84tXLhQSpcuLUeOHJE333xTHn300Ry3DwoKkuN/T9QeFhYm+//+9rx7924pr38N2tCkSRNZ+Pd7tlevXtK+fXvZtGmT9O3bVx544AGHHsNXX31l9y8nfn5+curvicX1z+OBAwckMDDQoTrkR4MGDeTFF1/Msn7o0KHSMGOSdpPp10+dnjMmvCxRQqR1a9XTx/o0m13Dj4g65T76qGrQCQlRp88vvlBlCxeq+e1DQtT8DI0aqQ+bIur02ayZmrPA31/Fx/z59utbvnxmffV/emvXqi8LPj5qcuVZs7LuZ+pUFTU+Pqq+W7dmvc/Fi+p4584Z18fEqN9DqlVTXyLsee01NQWercfg46Pq0bOnihnrx5Fdw09OMTJ2rIpkf39Vz27dVIOYiPp/efBB9X8REqLicOfOzH3/9puKHrPN78CsyMSsyL+imBW5df68ujZJ+fLqPFemjGqg0X/fPH1arQsMVB/P//EPYwP8yZNqDh1/f/VR9pNPVGboG362bFHZ4eubNQv0jh5VvydHRKgfB2rXztrAnZKi5hSKiFD1ad06+x8iXPE1I8P8+aqXacaUqbVrq4YYfQ58+qnKNm9v9fHf+nFMnKgGbPj7S7Zf/0REBg9WPyAA6muBiGo88/YWKcpzlTMrMjEr8s9dWWHnVEjZWrZMfXIuSpP0ffqp+umkEKlWrZpMnDgxy/qPPvpIqmX8rJSDjRs3SlBQkAwePFj8/PzkpZdekjZt2khgYKD88ccfDu3DGa37vr6+2mzvzz33nLz09yebEydOOHSSj4yMlIN/92OuXr261rVw9+7dDp3cVqxYIT/+Pcbm2LFjcv/994vFYpHixYtrVwJwtbp168rcuXNFxPg8xsTESJN70Pd43bp1EhgYKNWrV5eBAwfKwIEDpXr16hIUFJSrX3uIMly4oBpfCsmFEqVnT9Xjy2yYFZmYFfnHrKD8KohfM157LeswsqKGWZGJWZF/7soKL9fNHmRSnTqp2UnPnVOzkxYF3t7A1KnurkWunDhxAl26dMmyvmvXrnjjjTcc2keTJk2we/dujB8/HrVq1cIvv/yCunXrYsuWLahVq5ZD+6hfvz6WL1+OF198EQBgsVgAAF9++SUeeeQRh/YRGRmJgwcPolSpUlixYgU+++wzAMDNmzfh6emZ4/aNGjXCpk2bUL16dXTs2BGvvPIK9u3bhwULFqBRo0Y5bt+uXTttuXLlyjh8+DDi4+MRHh6uPZ7sJCYmIiQkRFu2J+N+trz99tvo168fzp07h/T0dCxYsABHjhzBnDlzsGzZshwfA6Amcvv444/x3Xff4cyZM0ixms0wPmMC82w0a9YMR48exbRp03D475kpe/TogX/9618oXbq0Q8cn0itZEpgxQ00OWr68u2tjX0qKmjj13/92d02cj1mRiVmhMCvInQri14yICMCBOXtNjVmRiVmhFMqscFmTEpEbVapUSaZn01f2s88+k8q2LlnvAs5o3R89erSEhoZKtWrVpFy5cnL79m0REZkxY4ZDE5DFxcVpk6glJSXJ//3f/0mtWrWkR48eWjdHV/Dw8JC//u7TrL9Mo/4vp0tf6m3YsEFat24tJUqUEH9/f2ncuLGszG6SdxveeustKVWqlHz44Yfi5+cnY8eOlUGDBkmxYsVk8uTJeXqMRFS4MSsyMSsUZgURWWNWZGJWKIUxKywiub1GO1HB99lnn+Hll1/GwIED8eijjwIANm/ejK+++gqTJ0/G//3f/+W4j507d8Lb21trhV+8eDFmzZqFGjVqYMyYMfDx8XGoLnFxcRg/fjz27NmDpKQk1K1bF//5z38cbt0HgB9++AFnz57FP/7xD5QtWxYAMHv2bISFhaFbt24O78dRPXr0cPi+CxYsyHb9+vXr0bhxY3h5eWH9+vV299GsWTO75X/++af2uK1t3brVoV8YKlWqhClTpqBTp04IDg7G7t27tXVbt27Ft99+a3f7hIQEzJgxA4cOHQIAPPDAAxg4cCBCQ0NzPDYRFUzMivxhVmTFrCAyH2ZF/jArsnJHVrDhh0xr4cKF+Oijj7Q3VPXq1TFixAiHT2gPP/wwRo4ciSeeeAInTpxAjRo10KNHD2zfvh2dOnXCpEmTXFj77N2+fRt+fn552nbHjh2Gk8tDDz1k874DBgzQlkUECxcuRGhoKOrXr6/tKyEhAT169MCsWbPyVJ/cqFGjBjZt2oT77rvPsH7z5s3o1KkTEhISctxHYGAgDh06hHLlyqFUqVJYvnw56tatixMnTuChhx7C9evXbW77xx9/oF27dvD390eDBg0AANu3b8etW7e0rrpEVDgxK4yYFcwKIsqKWWHErCiEWeHG3kZEBVpISIg2a/348eOlbdu2IiKyadMmKVu2rEP7aNWqlcyaNUuuX7+e53rcvXtXYmNjpXTp0uLp6alNQPbmm29ql2O056+//pIWLVqIxWLRLjdosVikZcuWcunSpRy3f+211+TZZ5+Vu7rrd9+9e1eef/55efXVVx1+HNeuXZOVK1fK3LlzZfbs2Ya/nAwYMEDq1asniYmJ2rr169dLSEhItpPtZadq1aqy9e9LNDVu3Fjee+89ERGZN2+elChRwu62TZo0kf79+0tqaqq2LjU1Vfr16ydNmzZ16PhEZE7MCoVZwawgItuYFQqzwn1ZwYYfMqWKFSvKlStXsqy/du2aVKxY0aF9BAcHy9GjR0VEpHXr1jJp0iQRETl9+rT4+fk5tI9hw4ZJyZIlxd/fX5588klZtGiRpKSkOPgolJiYGImOjpavv/7aMJv/vHnzHBqL27NnT6lfv742A7+Iulxh/fr15Wnr60lno3jx4nL48OEs6w8fPiz33XefQ49hyZIlEhwcLBaLRUJDQyUsLEz7Cw8Pz3H7tLQ06d69uzRr1kxu374ta9askaCgIO3/xBH/+c9/ZNzflyWaN2+eeHl5SeXKlcXHx0f+85//2N3Wz89PDh06lGX9gQMHxN/f3+E6EFHBwqzIxKxQmBVEZI1ZkYlZoRTGrGDDD5mSxWLRJgDTu3jxovj4+Di0jxYtWkjfvn1lzpw54u3tLceOHRMRdQm+8uXLO1yXtLQ0WblypfTr109CQkIkPDxcnnvuOVm3bp1D21eqVElWrVolIsZLDh46dEjCwsJy3D4kJES2bduWZf3vv/8uoaGhOW4fFhYmixYtyrJ+0aJFDh1fRKRKlSry0ksvSXJyskP3z86dO3ekdevW8uijj0pQUJBMnTo1z/sSEdmyZYt89NFHsmTJkhzvGxERke2EbytWrJCIiIh81YOI3IdZkYlZkT1mBRExKzIxK7JXGLKCl3MnU1myZIm2vHLlSsMEWWlpaVi9ejUqVKjg0L4mTZqE3r17Y9GiRRg1ahQqV64MQE2IljGxmyM8PDzQtm1btG3bFtOnT8fSpUsxbtw4zJgxA2lpaTluf+7cOe3Yeunp6UhNTc1x+/T0dHh7e2dZ7+3tjfT09By3HzBgAAYNGoS4uDhtHOrvv/+O8ePHG8bs2nPu3DkMGzYMAQEBDt0fAPbu3Ztl3ZgxY9CrVy/06dMHjz32mHaf2rVr57i/q1evolixYgCAs2fP4qeffsKtW7e08cX2PPXUUxg0aBA+/PBDw6R+I0aMQK9evRx+TERUMDArsmJWKMwKIsrArMiKWaEUyqxwWZMSkRtYLBbtUn4Zyxl/Pj4+UrVqVVm6dGm+jnHr1q1cd6sUEblw4YJ8/PHHUq9ePbFYLNKwYUOHtqtbt67MnTtXRIwt8zExMdKkSZMct+/atas89thjcu7cOW3dn3/+Kc2aNZPHH388x+3T0tJkwoQJUrp0ae25LF26tEyYMMEwPtee7t27y/z58x26b4bs/h/1tx29bOPevXulfPny4uHhIffff7/s2rVLIiMjJSgoSEJCQsTT01MWLlxodx937tyRYcOGiY+Pj3bJSF9fX3n55Ze1y2ASUeHBrMiKWcGsICIjZkVWzIrCmxW8qheZUsWKFbF9+3YUL17crfVITEzEjz/+iG+//Rbr1q1DdHQ0evfujd69e6NSpUoO7WPx4sXo168fXn/9dcTGxiImJgZHjhzBnDlzsGzZMrRp08bu9mfPnkXXrl1x4MABREVFaetq1qyJJUuW2Lycoa3HAwAhISE53lf/K8nly5cRGxuLAQMGoFatWll+KejatWuW7U+fPu1wvcqXL2+zrEOHDvDy8sLIkSMxd+5cLFu2DO3atcN///tfAMCLL76IHTt2YOvWrTke5+bNm4iLiwOgLuOYm18aiKjgYVZkYlYwK4goe8yKTMyKwpsVbPghU+nbty+6deuGdu3aISgoKF/78vDwgMVisVnuSHdKf39/hIeH46mnnkLv3r0d6v6XnY0bNyI2NhZ79uxBUlIS6tati7fffhtt27Z1aHsRwapVq3D48GEA6hKUrVu3zlNdHOXh4eHQ/SwWi0PPZV4VL14ca9asQe3atZGUlISQkBBs374d9erVAwAcPnwYjRo1cujSjYAKNwBa2BFR4cOsyB6zgllBRJmYFdljVhTOrOAcP2QqlStXxrvvvos+ffqgefPm6Nq1K7p27YoyZcrkel8LFy403E5NTcWuXbswe/ZsxMTEOLSPJUuWoFWrVg6frKzdvXsX7777LgYOHIhff/01V9uuWbMGQ4cOxdatWxESEoI2bdporfjXr1/HAw88gOnTp6Np06Z29/PXX3/h1VdfxerVq3Hp0iVYtxXbOrk6Ms43tw4ePIgzZ84gJSXFsD67lv0M8fHxKFmyJAAgKCgIgYGBCA8P18rDw8Nx48YNu8e9e/cuYmJiMGXKFCQlJWn7evHFFzF69OhsxzoTUcHFrMjErFCYFURkjVmRiVmhFOqscNkgMiI3OnPmjEybNk3atm0rvr6+UrduXYmJiZFdu3ble9/ffPONdO3aNVfbXLp0STZu3CgbN26US5cu5WrbwMBAOXnyZK62ERHp0qWLTJw40Wb55MmTHRqL2759e6lRo4Z8+umnsnDhQlm0aJHhz57ffvsty9jn2bNnS4UKFaREiRLy3HPPOTSWNS4uTmrXrp3teNycxuJaLBbDcx4UFCQnTpzQbl+8eDHHfQwePFgiIiJk+vTpsmfPHtmzZ49Mnz5dSpYsKYMHD86x/kRUMDErmBUZmBVEZAuzglmRoTBnBRt+yPQSExNl/vz58s9//lPCw8OlXLlyMmTIENm/f3+e9hcXFyeBgYEO3Tc5OVkGDBggnp6e2knFy8tLBg4c6PAlCLt27SpfffVVrutZrlw5OXjwoM3yQ4cOSVRUVI77CQoKynOwtWvXTsaPH6/d3rt3r3h5ecmzzz4rH330kZQsWVJGjx6d4346d+4s3bp1k8uXL0tQUJAcPHhQNm7cKA0aNJANGzbY3dZisUjHjh2le/fu0r17d/Hy8pK2bdtqtzt27JjjCTokJER++umnLOuXL18uISEhOdafiAo+ZkX2mBXMCiLKxKzIHrOi4GcFh3qR6QUHB6Nnz57o2bMn0tLSsH79eixevBhbtmzBAw88kKt93bp1C1OmTHG4i+e///1vrF+/HkuXLkXjxo0BAJs2bcKwYcPwyiuv4LPPPstxHx06dMDIkSOxb98+1KtXD4GBgYZyW90R//rrL7tdBb28vHD58uUcjx8VFZWlG6aj9uzZg3feeUe7PW/ePDRs2FCbAC0qKgqjR4/GmDFj7O5ny5YtWLNmDYoXLw4PDw94eHigSZMmeO+99zBs2DDs2rXL5rb9+vUz3O7Tp0+W+/Tt29fu8X19fbO9XGfFihXh4+Njd1siKhyYFdljVmRiVhARsyJ7zIpMBTYrXNakRORGLVq0kGvXrmVZf/36dWnRooVD+wgLC5Pw8HDtLywsTDw9PSU4OFgWL17s0D6KFSsma9euzbJ+zZo1Urx4cYf2YX35SOvLENoSHR1t93KCP/74o1SsWDHH469cuVLatm2bp26hvr6+cubMGe1248aN5Z133tFunzx5UoKCgnLcT1hYmNaNMjo6WtasWSMiIsePHxd/f/9c1yu3YmJipFevXobuo7dv35bevXvLmDFjXH58InINZgWzwpmYFUTmxKxgVjiTu7KCPX7IlNatW5dloi4AuH37NjZu3OjQPiZNmmS47eHhgRIlSqBhw4aGSbzsuXnzJiIjI7Osj4iIwM2bNx3aR14nM+vYsSPeeusttG/fHn5+foayW7duYfTo0ejcuXOO+3nqqadw8+ZN7TKD1q398fHxNreNjIzEyZMnERUVhZSUFOzcudMwgd2NGzccmsCsZs2a2LNnDypWrIiGDRvi/fffh4+PD7744gtER0fnuH1e9OjRw3B71apVKFu2LOrUqQNA/eqQkpKCVq1aueT4ROR6zApmRX4xK4jMj1nBrMivgpAVbPghU9m7d6+2fPDgQVy8eFG7nZaWhhUrVjjcndK6K19ePPLIIxg9ejTmzJmjnSRv3bqFmJgYPPLIIzlun56ejq+++goLFizAqVOnYLFYEB0djSeeeALPPPOM3ctCvvnmm1iwYAGqVq2KoUOH4v777wegLjM4bdo0pKWlYdSoUTnWwTqocqNjx44YOXIkJkyYgEWLFiEgIMAw2//evXtRqVKlHPfz5ptvIjk5GQAQGxuLzp07o2nTpihWrBjmz5+f5/rZExoaarj9xBNPGG7zEr1EhRezIhOzIn+YFUTmxazIxKzInwKRFS7rS0TkBvoZ2bPrwhgQECAzZsxweH/Xrl2TDz/8UAYNGiSDBg2SiRMnSkJCgsPb79u3T0qXLi3FihWTli1bSsuWLaVYsWJSpkyZHCeBS09Pl06dOonFYpEHH3xQnn76aXnqqae0Wei7deuW4/FPnTolHTp0yDJjfYcOHQwz0LvK5cuXpWnTpmKxWCQ4OFgWLFhgKG/ZsqW88cYbedr31atXJT093RnVJKIihllhxKwgIsqKWWHErCjcLCJ5nF2JqAA6ffo0RATR0dHYtm0bSpQooZX5+PggIiICnp6eDu3rjz/+QLt27eDv748GDRoAALZv345bt27hl19+Qd26dR3az82bN/HNN9/g8OHDAIDq1aujd+/e8Pf3t7vdrFmz8NJLL2Hx4sVo0aKFoWzNmjV4/PHH8cknn+Q4gRgAXLt2DcePH4eIoEqVKg53KbV2+/btLF1dQ0JCctzu+vXrCAoKyvLcx8fHIygoyOGJzI4fP464uDg89thj8Pf3h4jY/XWCiCg7zIrsMSuIiDIxK7LHrCik3NTgROQyKSkp0r9//3y3PDdp0kT69+8vqamp2rrU1FTp16+fNG3aNL/VzFGbNm3kvffes1k+btw4adu2rcvrkZSUJEOGDJESJUpov3ro/+6FK1euSMuWLbVfFuLi4kREZMCAATJ8+PB7UgciMhdmhXMxK4jIjJgVzsWscB/2+CFTCgsLw65du1CxYsU878Pf3x+7du1CtWrVDOsPHjyI+vXr25xEbcmSJQ4fw9YlEwGgZMmSWLFiBR588MFsy3ft2oUOHToYxhu7wpAhQ7B27VqMHTsWzzzzDKZNm4Zz587h888/x/jx49G7d2+XHh9Ql0W8dOkSvvzyS1SvXh179uxBdHQ0Vq5cieHDh+PAgQMurwMRmQ+zwnmYFURkVswK52FWuJG7W56IXKFv374yceLEfO0jIiJCVq5cmWX9ihUrJCIiwuZ29i6T6OglE0VEvL295fz58zbLz507Jz4+Po4/oDyKiorSLh0ZHBwsx44dExGROXPmSIcOHVx+fBGRyMhI2b17t4iIBAUFaS3zcXFxEhgYeE/qQETmw6xwHmYFEZkVs8J5mBXuw6t6kSlVqVIFsbGx2Lx5M+rVq4fAwEBD+bBhw3Lcx1NPPYVBgwbhww8/xKOPPgoA2Lx5M0aMGIFevXrZ3C6vl0m0lpaWBi8v229RT09P3L171ynHsic+Pl67tGFISIh2mcUmTZrghRdecPnxASA5ORkBAQHZ1s3X1/ee1CHD7du3s1zGkogKJ2aF8zArjJgVRObBrHAeZoXRvcwKNvyQKc2YMQNhYWHYsWMHduzYYSizWCwOnaA//PBDWCwW9O3bVzsRent744UXXsD48eNdUm89EUH//v1tnoDu3Lnj8joAQHR0NE6ePIly5cqhWrVq+O6779CgQQMsXboUYWFh96QOTZs2xZw5czB27FgA6v8wPT0d77//fpYJ6lwhPT0d48aNw/Tp0/HXX3/h6NGjiI6OxltvvYUKFSpg0KBBLq8DETkfs8J5mBXMCiKzYlY4D7PCfVnBOX6IspGWlobNmzejVq1a8PX1RVxcHACgUqVK2bYQ602ZMsXh49gLigEDBji0j1mzZjl8vLz4+OOP4enpiWHDhmHVqlXo0qULRASpqamYOHEiXnrpJZceHwD279+PVq1aoW7dulizZg26du2KAwcOID4+Hps3b0alSpVcevzY2FjMnj0bsbGxeO6557B//35ER0dj/vz5mDRpErZs2eLS4xNRwcSsyMSsYFYQUfaYFZmYFe7LCjb8ENng5+eHQ4cO5XoiN0fvb7FYcOLEibxUza1Onz6NHTt2oHLlyqhdu/Y9O+7169fxySefYM+ePUhKSkLdunUxZMgQlCpVyuXHrly5Mj7//HO0atUKwcHB2iRwhw8fxiOPPIJr1665vA5EVDAxK7LHrGBWEFEmZkX2mBX3Lis41ItMY/jw4Rg7diwCAwMxfPhwu/edOHFijvurWbMmTpw4kesT9MmTJ3N1/4Jqy5YtuHr1Kjp37qytmzNnDkaPHo3k5GQ8/vjjmDp16j0bCxsaGopRo0bdk2NZO3fuHCpXrpxlfXp6OlJTU91QIyLKK2aFczErMjEriMyDWeFczIpM7soKNvyQaezatUt7s+zatcvm/SwWi0P7e+edd/Dqq69i7Nix2U7kFhISkvfKFgKxsbFo3ry5doLet28fBg0ahP79+6NGjRp4//33Ubp0aYwZM+ae1CchIQHbtm3DpUuXskx017dvX5ceu0aNGti4cSPKly9vWP/DDz/goYcecumxici5mBXOxazIxKwgMg9mhXMxKzK5Kys41IvISmxsLF555RUEBwdr6/QndRGBxWJBWlpajvsaOHCg3fKZM2fmvaIuVqpUKSxduhT169cHAIwaNQrr16/Hpk2bAADff/89Ro8ejYMHD7q8LkuXLkXv3r2RlJSEkJAQw/+HxWLRrgjgKosXL0a/fv3w+uuvIzY2FjExMThy5AjmzJmDZcuWoU2bNi49PhEVPMwKhVmRiVlBRNaYFQqzIpO7soINP0RWPD09ceHCBRw6dMju/Zo1a5bjvrp37264nZqaiv379yMhIQEtW7bEggUL8lVXV/Lz88OxY8cQFRUFQF1msUOHDlq3yFOnTqFWrVq4ceOGy+tStWpVdOzYEe+++26Ok+C5ysaNGxEbG2sYC/z222+jbdu2bqkPEbkXs0JhVhgxK4hIj1mhMCuM3JEVHOpFppScnIzx48dj9erV2Xbhszf5WUZbqCMn4JwsXLgwy7r09HS88MILLp8xPr8iIyNx8uRJREVFISUlBTt37kRMTIxWfuPGDXh7e9+Tupw7dw7Dhg1z28kZUJd+/PXXX912fCJyPmZF/jErjJgVRObDrMg/ZoWRO7KCDT9kSs8++yzWr1+PZ555BqVKlXJ4/G2G3N4/Nzw8PDB8+HA0b94cr732msuOk18dO3bEyJEjMWHCBCxatAgBAQFo2rSpVr537957FjLt2rXDH3/8gejo6HtyPCIqGpgV+cesICKzY1bkH7PC/djwQ6b0888/Y/ny5WjcuHGetq9atWqOJ+n8jP+Mi4vD3bt387z9vTB27Fj06NEDzZo1Q1BQEGbPng0fHx+tfObMmfes63qnTp0wYsQIHDx4ELVq1cryi0DXrl2dfszw8HCHg9rVY4GJyDWYFfnHrGBWEJkdsyL/mBXuzwo2/JAphYeH47777svz9jExMQgNDc13Pawv/ygiuHDhApYvX45+/frle/+uVLx4cWzYsAHXr19HUFAQPD09DeXff/89goKC7kldnnvuOQBqgjxrjk6Il1uTJk1y+j6JqGBhVuQfs2KS0/dJRAULsyL/mBWTnL7P3OLkzmRKX3/9NRYvXozZs2fnevymh4cHLl68iIiIiHzXo0WLFrBYLNr4Xg8PD5QoUQItW7bEwIED4eXFttfC7tatW/D393d3NYgoD5gVdK8wK4gKL2YF3SuuzAq+Osg0HnroIUMXuuPHjyMyMhIVKlTI0oVv586dNvfjjHG46enp+OCDD3Dnzh2kpqaiZcuWGDNmDD/05dKWLVtw9epVdO7cWVs3Z84cjB49GsnJyXj88ccxdepU+Pr6urQew4YNw5QpU7KsT05ORufOnbF27VqXHp+InIdZYT7MCiJyNmaF+RT1rGDDD5nG448/7pT9OKMT3Lhx4zBmzBi0bt0a/v7+mDJlCi5fvoyZM2c6oYZFR2xsLJo3b66doPft24dBgwahf//+qF69Oj744AOULl0aY8aMcWk9li9fjvDwcMPVB5KTk9G+fXuXHpeInI9ZYT7MCiJyNmaF+RT5rBAicrrKlSvL9OnTtdu//vqr+Pj4SFpamhtrVfiULFlStm/frt1+4403pHHjxtrt7777TqpXr+7yehw/flxKlSolH3/8sYiIJCYmyiOPPCJNmzaVpKQklx+fiMyJWeEczAoiMjNmhXMU9axgjx8ypbNnz8JisaBs2bIAgG3btuHbb79FjRo18Pzzz7v8+GfOnEHHjh21261bt4bFYsH58+e1OlHOrl27hsjISO32+vXr0aFDB+32ww8/jLNnz7q8HpUqVcKKFSvQokULeHh44H//+x98fX2xfPlyBAYGuvz4ROQazApzYFYQkSsxK8yhqGeFh8v2TORG//znP7XxkRcvXkTr1q2xbds2jBo1KtsZ3J3t7t278PPzM6zz9vZGamqqy49tJpGRkTh58iQAICUlBTt37kSjRo208hs3bmQZZ+0qtWvXxrJly/DGG28gICAAP//8Mz/IExVyzApzYFYQkSsxK8yhqGcFe/yQKe3fvx8NGjQAAHz33XeoVasWNm/ejF9++QWDBw/G22+/7dLjiwj69+9vmBzs9u3bGDx4sOFNvWDBApfWo7Dr2LEjRo4ciQkTJmDRokUICAhA06ZNtfK9e/eiUqVKLjm29aR+GXx9fXH+/Hk0btxYW2dvUj8iKriYFebArCAiV2JWmENRzwo2/JAppaamaifHVatWoWvXrgCAatWq4cKFCy4/fr9+/bKs69Onj8uPazZjx45Fjx490KxZMwQFBWH27Nnw8fHRymfOnIm2bdu65NjOmtSPiAouZoU5MCuIyJWYFeZQ1LPCIuKEqcaJCpiGDRuiRYsW6NSpE9q2bYutW7eiTp062Lp1K5588kn8+eef7q4i5cL169cRFBQET09Pw/r4+HgEBQUZTtpERI5iVpgLs4KIXIFZYS5FNSvY8EOmtG7dOnTv3h2JiYno16+fdrnDN954A4cPH2ZXSMqTlJQUXLp0Cenp6Yb15cqVc1ONiCg/mBXkCswKInNhVpAr3OusYMMPmVZaWhoSExMRHh6urTt16hQCAgIQERHhxppRYXP06FEMGjQIv/32m2G9iMBisSAtLc1NNSOi/GJWkLMwK4jMi1lBzuKurOAcP2Ranp6eCA8Px/jx4zF48GCEhYWhQoUK7q4WFUIDBgyAl5cXli1bhlKlSmU7ORsRFU7MCnIWZgWReTEryFnclRXs8UOmFxISgt27dyM6OtrdVaFCKjAwEDt27EC1atXcXRUichFmBeUXs4LI/JgVlF/uygqPe3o0Ijdg2yblV40aNXDlyhV3V4OIXIhZQfnFrCAyP2YF5Ze7soINP2QqsbGxuHnzprurQSYzYcIEvPbaa1i3bh2uXr2KxMREwx8RFS7MCnIFZgWRuTAryBXclRUc6kWm4unpiQsXLhgmWTt79ixKly6d5ZJ9RI7y8FBt5NZjcDlhJ1HhxKwgV2BWEJkLs4JcwV1ZwcmdyVSya8eMiopyQ03ITNauXevuKhCREzEryBWYFUTmwqwgV3BXVrDhh0yHV9EgZ2vWrJnNsv3799/DmhCRszAryNmYFUTmw6wgZ3NXVnCoF5mKh4cHQkNDczxJx8fH36MakRnduHED//vf//Dll19ix44d7L5PVMgwK+heYFYQFW7MCroX7lVWsMcPmU5MTAxCQ0PdXQ0yoQ0bNmDGjBn48ccfUbp0afTo0QPTpk1zd7WIKA+YFeQqzAoi82BWkKvc66xgww+ZztNPP22YhI0oPy5evIivvvoKM2bMQGJiInr27Ik7d+5g0aJFqFGjhrurR0R5xKwgZ2JWEJkTs4KcyZ1Zwcu5k6lwHC45U5cuXXD//fdj7969mDRpEs6fP4+pU6e6u1pElE/MCnImZgWROTEryJncnRXs8UOmwimryJl+/vlnDBs2DC+88AKqVKni7uoQkZMwK8iZmBVE5sSsIGdyd1awxw+ZSnp6OrtjktNs2rQJN27cQL169dCwYUN88sknuHLlirurRUT5xKwgZ2JWEJkTs4Kcyd1Zwat6ERHlIDk5GfPnz8fMmTOxbds2pKWlYeLEiRg4cCCCg4PdXT0iIioAmBVERJQTd2UFG36IiHLhyJEjmDFjBubOnYuEhAS0adMGS5YscXe1iIioAGFWEBFRTu5lVrDhh4goD9LS0rB06VLMnDmTH+aJiChbzAoiIsrJvcgKNvwQEREREREREZkUJ3cmIiIiIiIiIjIpNvwQEREREREREZkUG36IiIiIiIiIiEyKDT9ERERERERERCbFhh8iIiIiIiIiIpNiww8RERERERERkUmx4YeIiIiIiIiIyKT+H9yv6krM+f/cAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array_withLabel(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## 使用训练好的模型\n",
    "\n",
    "最后，使用训练好的模型对单个图像进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:07.431140Z",
     "iopub.status.busy": "2022-08-31T04:54:07.430605Z",
     "iopub.status.idle": "2022-08-31T04:54:07.434456Z",
     "shell.execute_reply": "2022-08-31T04:54:07.433847Z"
    },
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` 模型经过了优化，可同时对一个*批*或一组样本进行预测。因此，即便您只使用一个图像，您也需要将其添加到列表中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:07.437824Z",
     "iopub.status.busy": "2022-08-31T04:54:07.437357Z",
     "iopub.status.idle": "2022-08-31T04:54:07.440848Z",
     "shell.execute_reply": "2022-08-31T04:54:07.440287Z"
    },
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "现在预测这个图像的正确标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:07.444040Z",
     "iopub.status.busy": "2022-08-31T04:54:07.443636Z",
     "iopub.status.idle": "2022-08-31T04:54:07.505184Z",
     "shell.execute_reply": "2022-08-31T04:54:07.504495Z"
    },
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 32ms/step\n",
      "[[1.09913635e-05 1.53144684e-12 9.99127805e-01 9.52572132e-10\n",
      "  3.38109530e-04 8.31401448e-10 5.23094728e-04 4.54124927e-18\n",
      "  1.62285643e-10 8.01199862e-17]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:07.508520Z",
     "iopub.status.busy": "2022-08-31T04:54:07.508244Z",
     "iopub.status.idle": "2022-08-31T04:54:07.594023Z",
     "shell.execute_reply": "2022-08-31T04:54:07.593316Z"
    },
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`keras.Model.predict` 会返回一组列表，每个列表对应一批数据中的每个图像。在批次中获取对我们（唯一）图像的预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-31T04:54:07.597559Z",
     "iopub.status.busy": "2022-08-31T04:54:07.597282Z",
     "iopub.status.idle": "2022-08-31T04:54:07.601835Z",
     "shell.execute_reply": "2022-08-31T04:54:07.601262Z"
    },
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "该模型会按照预期预测标签。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(model.summary())\n",
    "model.save_weights('./dress_checkpoint/')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "classification.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "ee21c858e9e6cc3714526aa083051029b46bcc72805c0fea5b8193e35926ec97"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
